Systems and methods to skip inconsequential matrix operations

ABSTRACT

Disclosed embodiments relate to systems and methods to skip inconsequential matrix operations. In one example, a processor includes decode circuitry to decode an instruction having fields to specify an opcode and locations of first source, second source, and destination matrices, the opcode indicating that the processor is to multiply each element at row M and column K of the first source matrix with a corresponding element at row K and column N of the second source matrix, and accumulate a resulting product with previous contents of a corresponding element at row M and column N of the destination matrix, the processor to skip multiplications that, based on detected values of corresponding multiplicands, would generate inconsequential results, scheduling circuitry to schedule execution of the instruction; and execution circuitry to execute the instructions as per the opcode.

FIELD OF INVENTION

The field of invention relates generally to computer processorarchitecture, and, more specifically, to systems and methods to skipinconsequential matrix operations.

BACKGROUND

Matrices are increasingly important in many computing tasks such asmachine learning and other bulk data processing. Deep Learning is aclass of machine learning algorithms. Deep learning architectures, suchas deep neural networks, have been applied to fields including computervision, speech recognition, natural language processing, audiorecognition, social network filtering, machine translation,bioinformatics and drug design.

Inference and training, two tools used for deep learning, are tendingtowards low precision arithmetic. Maximizing throughput of deep learningalgorithms and computations may assist in meeting the needs of deeplearning processors, for example, those performing deep learning in adata center.

Matrix-matrix multiplication (a.k.a., GEMM or General MatrixMultiplication) is a common compute-heavy operation on today'sprocessors. Special hardware for matrix multiplication (e.g., GEMM) is agood option for improving the peak compute (and energy efficiency) ofcertain applications, such as deep learning.

Today's common hardware accelerators for neural networks perform mainlymatrix multiplication in a dense format, not considering the fact thatthere is a large percentage of zeros or close-to-zero values found inone (or both) matrices. This introduces inefficient usage of hardwareresources (wasting power and compute cycles to generate inconsequentialproducts). Hardware accelerators for neural networks, both for trainingand inference, strive to achieve the best raw performance numbers andpower to performance ratio values; exploiting the native and injectedsparsity in those neural networks is one way to help achieve thosegoals.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings, in which likereferences indicate similar elements and in which:

FIG. 1A illustrates an embodiment of configured tiles;

FIG. 1B illustrates an embodiment of configured tiles;

FIG. 2 illustrates several examples of matrix storage;

FIG. 3 illustrates an embodiment of a system utilizing a matrix (tile)operations accelerator;

FIGS. 4 and 5 show different embodiments of how memory is shared using amatrix operations accelerator;

FIG. 6 illustrates an embodiment of matrix multiply accumulate operationusing tiles (“TMMA”);

FIG. 7 illustrates an embodiment of a subset of the execution of aniteration of a chained fused multiply accumulate instruction;

FIG. 8 illustrates an embodiment of a subset of the execution of aniteration of a chained fused multiply accumulate instruction;

FIG. 9 illustrates an embodiment of a subset of the execution of aniteration of a chained fused multiply accumulate instruction;

FIG. 10 illustrates an embodiment of a subset of the execution of aniteration of chained fused multiply accumulate instruction;

FIG. 11 illustrates power-of-two sized SIMD implementations wherein theaccumulators use input sizes that are larger than the inputs to themultipliers according to an embodiment;

FIG. 12 illustrates an embodiment of a system utilizing matrixoperations circuitry;

FIG. 13 illustrates an embodiment of a processor core pipelinesupporting matrix operations using tiles;

FIG. 14 illustrates an embodiment of a processor core pipelinesupporting matrix operations using tiles;

FIG. 15 illustrates an example of a matrix expressed in row major formatand column major format;

FIG. 16 illustrates an example of usage of matrices (tiles);

FIG. 17 illustrates an embodiment a method of usage of matrices (tiles);

FIG. 18 illustrates support for configuration of the usage of tilesaccording to an embodiment;

FIG. 19 illustrates an embodiment of a description of the matrices(tiles) to be supported;

FIGS. 20(A)-(D) illustrate examples of register(s);

FIG. 21A is a block diagram illustrating execution of a matrix non-zerofused multiply-accumulate (TILENZFMA) instruction, according to someembodiments;

FIG. 21B is a block diagram illustrating execution of a matrix non-zeromultiplication (TILENZMUL) instruction according to some embodiments;

FIGS. 21C-E are block diagrams illustrating detection of inconsequentialmultiplications, according to some embodiments;

FIG. 21C is a block diagram illustrating detection of near-zeromultiplicands, according to some embodiments;

FIG. 21D is a block diagram illustrating detection of multiplicands neara constant value, according to some embodiments;

FIG. 21E is a block diagram illustrating detection of multiplicands neara mean value, according to some embodiments;

FIG. 22A is a block diagram illustrating execution circuitry to performa matrix non-zero fused multiply-accumulate (TILENZFMA) instruction,according to some embodiments;

FIG. 22B is a block diagram illustrating execution circuitry to performa matrix non-zero fused multiply-accumulate (TILENZFMA) instruction,according to some embodiments;

FIG. 22C is a block diagram illustrating an embodiment of matrixcompression;

FIG. 23 illustrates an embodiment of a processor executing a matrixnon-zero fused multiply-accumulate (TILENZFMA) instruction, according tosome embodiments;

FIG. 24 is a block diagram illustrating a format of a matrix non-zerofused multiply-accumulate (TILENZFMA) instruction, according to someembodiments;

FIGS. 25A-25B are block diagrams illustrating a generic vector friendlyinstruction format and instruction templates thereof according toembodiments;

FIG. 25A is a block diagram illustrating a generic vector friendlyinstruction format and class A instruction templates thereof accordingto embodiments;

FIG. 25B is a block diagram illustrating the generic vector friendlyinstruction format and class B instruction templates thereof accordingto embodiments;

FIG. 26A is a block diagram illustrating an exemplary specific vectorfriendly instruction format according to embodiments;

FIG. 26B is a block diagram illustrating the fields of the specificvector friendly instruction format that make up the full opcode fieldaccording to one embodiment;

FIG. 26C is a block diagram illustrating the fields of the specificvector friendly instruction format that make up the register index fieldaccording to one embodiment;

FIG. 26D is a block diagram illustrating the fields of the specificvector friendly instruction format that make up the augmentationoperation field according to one embodiment;

FIG. 27 is a block diagram of a register architecture according to oneembodiment;

FIG. 28A is a block diagram illustrating both an exemplary in-orderpipeline and an exemplary register renaming, out-of-orderissue/execution pipeline according to embodiments;

FIG. 28B is a block diagram illustrating both an exemplary embodiment ofan in-order architecture core and an exemplary register renaming,out-of-order issue/execution architecture core to be included in aprocessor according to embodiments;

FIGS. 29A-B illustrate a block diagram of a more specific exemplaryin-order core architecture, which core would be one of several logicblocks (including other cores of the same type and/or different types)in a chip;

FIG. 29A is a block diagram of a single processor core, along with itsconnection to the on-die interconnect network and with its local subsetof the Level 2 (L2) cache, according to embodiments;

FIG. 29B is an expanded view of part of the processor core in FIG. 29Aaccording to embodiments;

FIG. 30 is a block diagram of a processor that may have more than onecore, may have an integrated memory controller, and may have integratedgraphics according to embodiments;

FIGS. 31-34 are block diagrams of exemplary computer architectures;

FIG. 31 shown a block diagram of a system in accordance with oneembodiment of the present invention;

FIG. 32 is a block diagram of a first more specific exemplary system inaccordance with an embodiment of the present invention;

FIG. 33 is a block diagram of a second more specific exemplary system inaccordance with an embodiment of the present invention;

FIG. 34 is a block diagram of a System-on-a-Chip (SoC) in accordancewith an embodiment of the present invention; and

FIG. 35 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according toembodiments.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth.However, it is understood that embodiments may be practiced withoutthese specific details. In other instances, well-known circuits,structures and techniques have not been shown in detail in order not toobscure the understanding of this description.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

In many mainstream processors, handling matrices is a difficult and/orinstruction intensive task. For example, rows of a matrix could be putinto a plurality of packed data (e.g., SIMD or vector) registers andthen operated on individually. For example, an add two 8×2 matrices mayrequire a load or gather into four packed data registers depending upondata sizes. Then a first add of packed data registers corresponding to afirst row from each matrix is performed and a second add of packed dataregisters corresponding to a second row from each matrix is performed.Then the resulting packed data registers are scattered back to memory.While for small matrices this scenario may be acceptable, it is oftennot acceptable with larger matrices.

DISCUSSION

Described herein are mechanisms to support matrix operations in computerhardware such as central processing units (CPUs), graphic processingunits (GPUs), and accelerators. The matrix operations utilize2-dimensional (2-D) data structures representing one or more packedregions of memory such as registers. Throughout this description, these2-D data structures are referred to as tiles. Note that a matrix may besmaller than a tile (use less than all of a tile) or utilize a pluralityof tiles (the matrix is larger than the size of any one tile).Throughout the description, matrix (tile) language is used to indicateoperations performed using tiles that impact a matrix; whether or notthat matrix is larger than any one tile is not typically relevant.

Each tile may be acted upon by different operations such as those thatare detailed herein and include, but are not limited to: matrix (tile)multiplication, tile add, tile subtract, tile diagonal, tile zero, tiletransform, tile dot product, tile broadcast, tile row broadcast, tilecolumn broadcast, tile multiplication, tile multiplication andaccumulation, tile move, etc. Additionally, support for operators suchas the use of a scale and/or bias may be used with these operations orin support of non-numeric applications in the future, for instance,OpenCL “local memory,” data compression/decompression, etc. Alsodescribed herein are instructions for performing matrix (tile) non-zerofused multiply-accumulate (TILENZFMA) and matrix (tile) non-zeromultiply (TILEMUL) instructions.

Portions of storage (such as memory (non-volatile and volatile),registers, cache, etc.) are arranged into tiles of different horizontaland vertical dimensions. For example, a tile may have horizontaldimension of 4 (e.g., four rows of a matrix) and a vertical dimension of8 (e.g., 8 columns of the matrix). Typically, the horizontal dimensionis related to element sizes (e.g., 2-, 4-, 8-, 16-, 32-, 64-, 128-bit,etc.). Multiple datatypes (single precision floating point, doubleprecision floating point, integer, etc.) may be supported.

Exemplary Usage of Configured Tiles

In some embodiments, tile parameters can be configured. For example, agiven tile may be configured to provide tile options. Exemplary tileoptions include but are not limited to: a number of rows of the tile, anumber of columns of the tile, whether the tile is VALID, and whetherthe tile consists of a PAIR of equal-sized tiles.

FIG. 1A illustrates an embodiment of configured tiles. As shown, 4 kB ofapplication memory 102 have stored thereon 4 1 kB titles, tile t0 104,tile t1 106, tile t2 108, and tile t3 110. In this example, the 4 tilesdo not consist of pairs, and each have elements arranged in rows andcolumns. Tile t0 104 and tile t1 106 have K rows and N columns of 4-byteelements (e.g., single precision data), where K equals 8 and N=32. Tilet2 108 and tile t3 110 have K rows and N/2 columns of 8-byte elements(e.g., double precision data). As the double precision operands aretwice the width of single precision, this configuration is consistentwith a palette, used to provide tile options, supplying at least 4 nameswith total storage of at least 4 kB. In operation, the tiles can beloaded from and stored to memory using load and store operations.Depending upon the instruction encoding scheme used, the amount ofavailable application memory, as well as the size, number, andconfiguration of available tiles varies.

FIG. 1B illustrates an embodiment of configured tiles. As shown, 4 kB ofapplication memory 122 have stored thereon 2 pairs of 1 kB-titles, thefirst pair being tile t4L 124 and tile t4R 126, and the second pairbeing tile t5L 128 and tile t5R 130. As shown the pairs of tiles aredivided into a left tile and a right tile. In other embodiments, thepair of tiles are divided into an even tile and an odd tile. In thisexample, the 4 tiles each have elements arranged in rows and columns.Tile t4L 124 and tile t4R 126 have K rows and N columns of 4-byteelements (e.g., single precision floating point data), where K equals 8and N equals 32. Tile t5L 128 and tile t5R 130 have K rows and N/2columns of 8-byte elements (e.g., double precision floating point data).As the double precision operands are twice the width of singleprecision, this configuration is consistent with a palette, used toprovide tile options, supplying at least 2 names with total storage ofat least 4 kB. The four tiles of FIG. 1A use 4 names, each naming a 1 kBtile, whereas the 2 pairs of tiles in FIG. 1B can use 2 names to specifythe paired tiles. In some embodiments, tile instructions accept a nameof a paired tile as an operand. In operation, the tiles can be loadedfrom and stored to memory using load and store operations. Dependingupon the instruction encoding scheme used, the amount of availableapplication memory, as well as the size, number, and configuration ofavailable tiles varies.

In some embodiments, tile parameters are definable. For example, a“palette” is used to provide tile options. Exemplary options include,but are not limited to: the number of tile names, the number of bytes ina row of storage, the number of rows and columns in a tile, etc. Forexample, a maximum “height” (number of rows) of a tile may be definedas:Tile Max Rows=Architected Storage/(The Number of Palette Names*TheNumber of Bytes per row).

As such, an application can be written such that a fixed usage of nameswill be able to take advantage of different storage sizes acrossimplementations.

Configuration of tiles is done using a tile configuration (“TILECONFIG”)instruction, where a particular tile usage is defined in a selectedpalette. This declaration includes the number of tile names to be used,the requested number of rows and columns per name (tile), and, in someembodiments, the requested datatype of each tile. In some embodiments,consistency checks are performed during the execution of a TILECONFIGinstruction to determine that it matches the restrictions of the paletteentry.

Exemplary Tile Storage Types

FIG. 2 illustrates several examples of matrix storage. In (A), a tile isstored in memory. As shown, each “row” consists of four packed dataelements. To get to the next “row,” a stride value is used. Note thatrows may be consecutively stored in memory. Strided memory accessesallows for access of one row to then next when the tile storage does notmap the underlying memory array row width.

Tile loads from memory and stores to memory are typically stridedaccesses from the application memory to packed rows of data. ExemplaryTILELOAD and TILESTORE instructions, or other instruction references toapplication memory as a TILE operand in load-op instructions, are, insome embodiments, restartable to handle (up to) 2*rows of page faults,unmasked floating point exceptions, and/or interrupts per instruction.

In (B), a matrix is stored in a tile comprised of a plurality ofregisters such as packed data registers (single instruction, multipledata (SIMD) or vector registers). In this example, the tile is overlaidon three physical registers. Typically, consecutive registers are used,however, this need not be the case.

In (C), a matrix is stored in a tile in non-register storage accessibleto a fused multiple accumulate (FMA) circuit used in tile operations.This storage may be inside of an FMA, or adjacent to it. Additionally,in some embodiments, discussed below, the storage may be for a dataelement and not an entire row or tile.

The supported parameters for the TMMA architecture are reported viaCPUID. In some embodiments, the list of information includes a maximumheight and a maximum SIMD dimension. Configuring the TMMA architecturerequires specifying the dimensions for each tile, the element size foreach tile and the palette identifier. This configuration is done byexecuting the TILECONFIG instruction.

Successful execution of a TILECONFIG instruction enables subsequent TILEoperators. A TILERELEASEALL instruction clears the tile configurationand disables the TILE operations (until the next TILECONFIG instructionsexecutes). In some embodiments, XSAVE, XSTORE, etc. are used in contextswitching using tiles. In some embodiments, 2 XCRO bits are used inXSAVE, one for TILECONFIG metadata and one bit corresponding to actualtile payload data.

TILECONFIG not only configures the tile usage, but also sets a statevariable indicating that the program is in a region of code with tilesconfigured. An implementation may enumerate restrictions on otherinstructions that can be used with a tile region such as no usage of anexisting register set, etc.

Exiting a tile region is typically done with the TILERELEASEALLinstruction. It takes no parameters and swiftly invalidates all tiles(indicating that the data no longer needs any saving or restoring) andclears the internal state corresponding to being in a tile region.

In some embodiments, tile operations will zero any rows and any columnsbeyond the dimensions specified by the tile configuration. For example,tile operations will zero the data beyond the configured number ofcolumns (factoring in the size of the elements) as each row is written.For example, with 64-byte rows and a tile configured with 10 rows and 12columns, an operation writing FP32 elements would write each of thefirst 10 rows with 12*4 bytes with output/result data and zero theremaining 4*4 bytes in each row. Tile operations also fully zero anyrows after the first 10 configured rows. When using 1K tile with 64-byterows, there would be 16 rows, so in this example, the last 6 rows wouldalso be zeroed.

In some embodiments, a context restore instruction (e.g., XRSTOR), whenloading data, enforces that the data beyond the configured rows for atile will be maintained as zero. If there is no valid configuration, allrows are zeroed. XRSTOR of tile data can load garbage in the columnsbeyond those configured. It should not be possible for XRSTOR to clearbeyond the number of columns configured because there is not an elementwidth associated with the tile configuration.

Context save (e.g., XSAVE) exposes the entire TILE storage area whenwriting it to memory. If XRSTOR loaded garbage data in to the rightmostpart of a tile, that data will be saved by XSAVE. XSAVE will write zerosfor rows beyond the number specified for each tile.

In some embodiments, tile instructions are restartable. The operationsthat access memory allow restart after page faults. The computationalinstructions that deal with floating point operations also allow forunmasked floating-point exceptions, with the masking of the exceptionscontrolled by a control and/or status register.

To support restarting instructions after these events, the instructionsstore information in the start registers detailed below.

Matrix (Tile) Operation Systems

Exemplary Hardware Support

FIG. 3 illustrates an embodiment of a system utilizing a matrix (tile)operations accelerator. In this illustration, a hostprocessor/processing system 301 communicates commands 311 (e.g., matrixmanipulation operations such as arithmetic or matrix manipulationoperations, or load and store operations) to a matrix operationsaccelerator 307. However, this is shown this way for discussion purposesonly. As detailed later, this accelerator 307 may be a part of aprocessing core. Typically, commands 311 that are tile manipulationoperator instructions will refer to tiles as register-register(“reg-reg”) or register-memory (“reg-mem”) format. Other commands suchas TILESTORE, TILELOAD, TILECONFIG, etc., do not perform data operationson a tile. Commands may be decoded instructions (e.g., micro-ops) ormacro-instructions for the accelerator 307 to handle.

In this example, a coherent memory interface 303 is coupled to the hostprocessor/processing system 301 and matrix operations accelerator 307such that they can share memory. FIGS. 4 and 5 show differentembodiments of how memory is shared using a matrix operationsaccelerator. As shown in FIG. 4, the host processor 401 and matrixoperations accelerator circuitry 405 share the same memory 403. FIG. 5illustrates an embodiment where the host processor 501 and matrixoperations accelerator 505 do not share memory but can access eachother's memory. For example, processor 501 can access tile memory 507and utilize its host memory 503 as normal. Similarly, the matrixoperations accelerator 505 can access host memory 503, but moretypically uses its own memory 507. Note these memories may be ofdifferent types.

In some embodiments, tiles are supported using an overlay over physicalregisters. For example, a tile may utilize 16 1,024-bit registers, 32512-bit registers, etc. depending on the implementation. In someembodiments, the matrix operations utilize 2-dimensional (2-D) datastructures representing one or more packed regions of memory such asregisters. Throughout this description, these 2-D data structures arereferred to as tiles or tile registers.

In some embodiments, the matrix operations accelerator 307 includes aplurality of FMAs 309 coupled to data buffers 305 (in someimplementations, one or more of these buffers 305 are stored in the FMAsof the grid as shown). The data buffers 305 buffer tiles loaded frommemory and/or tiles to be stored to memory (e.g., using a tileload ortilestore instruction). Data buffers may be, for example, a plurality ofregisters. Typically, these FMAs are arranged as a grid of chained FMAs309 which are able to read and write tiles. In this example, the matrixoperations accelerator 307 is to perform a matrix multiply operationusing tiles T0, T1, and T2. At least one of tiles is housed in the FMAgrid 309. In some embodiments, all tiles in an operation are stored inthe FMA grid 309. In other embodiments, only a subset is stored in theFMA grid 309. As shown, T1 is housed and T0 and T2 are not. Note that A,B, and C refer to the matrices of these tiles which may or may not takeup the entire space of the tile.

FIG. 6 illustrates an embodiment of matrix multiply accumulate operationusing tiles (“TMMA”).

The number of rows in the matrix (TILE A 601) matches the number ofserial (chained) FMAs comprising the computation's latency. Animplementation is free to recirculate on a grid of smaller height, butthe computation remains the same.

The source/destination vector comes from a tile of N rows (TILE C 605)and the grid of FMAs 611 performs N vector-matrix operations resultingin a complete instruction performing a matrix multiplication of tiles.Tile B 603 is the other vector source and supplies “broadcast” terms tothe FMAs in each stage.

In operation, in some embodiments, the elements of matrix B (stored in atile B 603) are spread across the rectangular grid of FMAs. Matrix B(stored in tile A 601) has its elements of a row transformed to match upwith the columnar dimension of the rectangular grid of FMAs. At each FMAin the grid, an element of A and B are multiplied and added to theincoming summand (from above in the Figure) and the outgoing sum ispassed to the next row of FMAs (or the final output).

The latency of a single step is proportional to K (row height of matrixB) and dependent TMMAs typically have enough source-destination rows(either in a single tile or across tile) to hide that latency. Animplementation may also split the SIMD (packed data element) dimension M(row height of matrix A) across time steps, but this simply changes theconstant that K is multiplied by. When a program specifies a smaller Kthan the maximum enumerated by the TMACC, an implementation is free toimplement this with “masking” or “early outs.”

The latency of an entire TMMA is proportional to N*K. The repeat rate isproportional to N. The number of MACs per TMMA instruction is N*K*M.

FIG. 7 illustrates an embodiment of a subset of the execution of aniteration of a chained fused multiply accumulate instruction. Inparticular, this illustrates execution circuitry of an iteration of onepacked data element position of the destination. In this embodiment, thechained fused multiply accumulate is operating on signed sources whereinthe accumulator is 2× the input data size.

A first signed source (source 1 701) and a second signed source (source2 703) each have four packed data elements. Each of these packed dataelements stores signed data such as floating-point data. A third signedsource (source 3 709) has two packed data elements, each of which storessigned data. The sizes of the first and second signed sources 701 and703 are half that of the third signed source (initial value or previousresult) 709. For example, the first and second signed sources 701 and703 could have 32-bit packed data elements (e.g., single precisionfloating point) while the third signed source 709 could have 64-bitpacked data elements (e.g., double precision floating point).

In this illustration, only the two most significant packed data elementpositions of the first and second signed sources 701 and 703 and themost significant packed data element position of the third signed source709 are shown. Of course, the other packed data element positions wouldalso be processed.

As illustrated, packed data elements are processed in pairs. Forexample, the data of the most significant packed data element positionsof the first and second signed sources 701 and 703 are multiplied usinga multiplier circuit 705, and the data from second most significantpacked data element positions of the first and second signed sources 701and 703 are multiplied using a multiplier circuit 707. In someembodiments, these multiplier circuits 705 and 707 are reused for otherpacked data elements positions. In other embodiments, additionalmultiplier circuits are used so that the packed data elements areprocessed in parallel. In some contexts, parallel execution is doneusing lanes that are the size of the signed third source 709. Theresults of each of the multiplications are added using additioncircuitry 711.

The result of the addition of the results of the multiplications isadded to the data from most significant packed data element position ofthe signed source 3 709 (using a different adder 713 or the same adder711).

Finally, the result of the second addition is either stored into thesigned destination 715 in a packed data element position thatcorresponds to the packed data element position used from the signedthird source 709 or passed on to the next iteration if there is one. Insome embodiments, a writemask is applied to this storage such that if acorresponding writemask (bit) is set, the storage happens, and, if notset, the storage does not happen.

FIG. 8 illustrates an embodiment of a subset of the execution of aniteration of a chained fused multiply accumulate instruction. Inparticular, this illustrates execution circuitry of an iteration of onepacked data element position of the destination. In this embodiment, thechained fused multiply accumulate is operating on signed sources whereinthe accumulator is 2× the input data size.

A first signed source (source 1 801) and a second signed source (source2 803) each have four packed data elements. Each of these packed dataelements stores signed data such as integer data. A third signed source(source 3 809) has two packed data elements, each of which stores signeddata. The sizes of the first and second signed sources 801 and 803 arehalf that of the third signed source 809. For example, the first andsecond signed sources 801 and 803 could have 32-bit packed data elements(e.g., single precision floating point) the third signed source 809could have 64-bit packed data elements (e.g., double precision floatingpoint).

In this illustration, only the two most significant packed data elementpositions of the first and second signed sources 801 and 803 and themost significant packed data element position of the third signed source809 are shown. Of course, the other packed data element positions wouldalso be processed.

As illustrated, packed data elements are processed in pairs. Forexample, the data of the most significant packed data element positionsof the first and second signed sources 801 and 803 are multiplied usinga multiplier circuit 805, and the data from second most significantpacked data element positions of the first and second signed sources 801and 803 are multiplied using a multiplier circuit 807. In someembodiments, these multiplier circuits 805 and 807 are reused for otherpacked data elements positions. In other embodiments, additionalmultiplier circuits are used so that the packed data elements areprocessed in parallel. In some contexts, parallel execution is doneusing lanes that are the size of the signed third source (initial valueor previous iteration result) 809. The results of each of themultiplications are added to the signed third source 809 usingaddition/saturation circuitry 813.

Addition/saturation (accumulator) circuitry 813 preserves a sign of anoperand when the addition results in a value that is too big. Inparticular, saturation evaluation occurs on the infinite precisionresult between the multi-way-add and the write to the destination ornext iteration. When the accumulator 813 is floating point and the inputterms are integer, the sum of products and the floating-pointaccumulator input value are turned into infinite precision values (fixedpoint numbers of hundreds of bits), the addition of the multiplicationresults and the third input is performed, and a single rounding to theactual accumulator type is performed.

Unsigned saturation means the output values are limited to a maximumunsigned number for that element width (all 1s). Signed saturation meansa value is limited to the be in the range between a minimum negativenumber and a max positive number for that element width (for bytes forexample, the range is from −128 (=−2{circumflex over ( )}7) to127(=2{circumflex over ( )}7−1)).

The result of the addition and saturation check is stored into thesigned result 815 in a packed data element position that corresponds tothe packed data element position used from the signed third source 809or passed on to the next iteration if there is one. In some embodiments,a writemask is applied to this storage such that if a correspondingwritemask (bit) is set, the storage happens, and, if not set, thestorage does not happen.

FIG. 9 illustrates an embodiment of a subset of the execution of aniteration of a chained fused multiply accumulate instruction. Inparticular, this illustrates execution circuitry of an iteration of onepacked data element position of the destination. In this embodiment, thechained fused multiply accumulate is operating on a signed source and anunsigned source wherein the accumulator is 4× the input data size.

A first signed source (source 1 901) and a second unsigned source(source 2 903) each have four packed data elements. Each of these packeddata elements has data such as floating point or integer data. A thirdsigned source (initial value or result 915) has a packed data element ofwhich stores signed data. The sizes of the first and second sources 901and 903 are a quarter of the third signed source 915. For example, thefirst and second sources 901 and 903 could have 16-bit packed dataelements (e.g., word) and the third signed source 915 could have 64-bitpacked data elements (e.g., double precision floating point or 64-bitinteger).

In this illustration, the four most significant packed data elementpositions of the first and second sources 901 and 903 and the mostsignificant packed data element position of the third signed source 915are shown. Of course, other packed data element positions would also beprocessed if there are any.

As illustrated, packed data elements are processed in quadruplets. Forexample, the data of the most significant packed data element positionsof the first and second sources 901 and 903 are multiplied using amultiplier circuit 905, data from second most significant packed dataelement positions of the first and second sources 901 and 903 aremultiplied using a multiplier circuit 907, data from third mostsignificant packed data element positions of the first and secondsources 901 and 903 are multiplied using a multiplier circuit 909, anddata from the least significant packed data element positions of thefirst and second sources 901 and 903 are multiplied using a multipliercircuit 911. In some embodiments, the signed packed data elements of thefirst source 901 are sign extended and the unsigned packed data elementsof the second source 903 are zero extended prior to the multiplications.

In some embodiments, these multiplier circuits 905-911 are reused forother packed data elements positions. In other embodiments, additionalmultiplier circuits are used so that the packed data elements areprocessed in parallel. In some contexts, parallel execution is doneusing lanes that are the size of the signed third source 915. Theresults of each of the multiplications are added using additioncircuitry 913.

The result of the addition of the results of the multiplications isadded to the data from most significant packed data element position ofthe signed source 3 915 (using a different adder 917 or the same adder913).

Finally, the result 919 of the second addition is either stored into thesigned destination in a packed data element position that corresponds tothe packed data element position used from the signed third source 915or passed to the next iteration. In some embodiments, a writemask isapplied to this storage such that if a corresponding writemask (bit) isset, the storage happens, and, if not set, the storage does not happen.

FIG. 10 illustrates an embodiment of a subset of the execution of aniteration of chained fused multiply accumulate instruction. Inparticular, this illustrates execution circuitry of an iteration of onepacked data element position of the destination. In this embodiment, thechained fused multiply accumulate is operating on a signed source and anunsigned source wherein the accumulator is 4× the input data size.

A first signed source 1001 and a second unsigned source 1003 each havefour packed data elements. Each of these packed data elements storesdata such as floating point or integer data. A third signed source 1015(initial or previous result) has a packed data element of which storessigned data. The sizes of the first and second sources are a quarter ofthe third signed source 1015 (initial or previous result). For example,the first and second sources could have 16-bit packed data elements(e.g., word) and the third signed source 1015 (initial or previousresult) could have 64-bit packed data elements (e.g., double precisionfloating point or 64-bit integer).

In this illustration, the four most significant packed data elementpositions of the first signed source 1001 and the second unsigned source1003 and the most significant packed data element position of the thirdsigned source 1015 are shown. Of course, other packed data elementpositions would also be processed if there are any.

As illustrated, packed data elements are processed in quadruplets. Forexample, the data of the most significant packed data element positionsof the first signed source 1001 and the second unsigned source 1003 aremultiplied using a multiplier circuit 1005, data from second mostsignificant packed data element positions of the first signed source1001 and the second unsigned source 1003 are multiplied using amultiplier circuit 1007, data from third most significant packed dataelement positions of the first signed source 1001 and the secondunsigned source 1003 are multiplied using a multiplier circuit 1009, anddata from the least significant packed data element positions of thefirst signed source 1001 and the second unsigned source 1003 aremultiplied using a multiplier circuit 1011. In some embodiments, thesigned packed data elements of the first signed source 1001 are signextended and the unsigned packed data elements of the second unsignedsource 1003 are zero extended prior to the multiplications.

In some embodiments, these multiplier circuits 1005-1011 are reused forother packed data elements positions. In other embodiments, additionalmultiplier circuits are used so that the packed data elements areprocessed in parallel. In some contexts, parallel execution is doneusing lanes that are the size of third signed source 1015 (initial orprevious result). The result of the addition of the results of themultiplications is added to the data from most significant packed dataelement position of third signed source 1015 (initial or previousresult) using adder/saturation 1013 circuitry.

Addition/saturation (accumulator) circuitry 1013 preserves a sign of anoperand when the addition results in a value that is too big or toosmall for signed saturation. In particular, saturation evaluation occurson the infinite precision result between the multi-way-add and the writeto the destination. When the accumulator 1013 is floating point and theinput terms are integer, the sum of products and the floating-pointaccumulator input value are turned into infinite precision values (fixedpoint numbers of hundreds of bits), the addition of the multiplicationresults and the third input is performed, and a single rounding to theactual accumulator type is performed.

The result 1019 of the addition and saturation check is stored into thesigned destination in a packed data element position that corresponds tothe packed data element position used from third signed source 1015(initial or previous result) or passed to the next iteration. In someembodiments, a writemask is applied to this storage such that if acorresponding writemask (bit) is set, the storage happens, and, if notset, the storage does not happen.

FIG. 11 illustrates power-of-two sized SIMD implementations wherein theaccumulators use input sizes that are larger than the inputs to themultipliers according to an embodiment. Note the source (to themultipliers) and accumulator values may be signed or unsigned values.For an accumulator having 2× input sizes (in other words, theaccumulator input value is twice the size of the packed data elementsizes of the sources), table 1101 illustrates different configurations.For byte sized sources, the accumulator uses word or half-precisionfloating-point (HPFP) values that are 16-bit in size. For word sizedsources, the accumulator uses 32-bit integer or single-precisionfloating-point (SPFP) values that are 32-bit in size. For SPFP or 32-bitinteger sized sources, the accumulator uses 64-integer ordouble-precision floating-point (DPFP) values that are 64-bit in size.

For an accumulator having 4× input sizes (in other words, theaccumulator input value is four times the size of the packed dataelement sizes of the sources), table 1103 illustrates differentconfigurations. For byte sized sources, the accumulator uses 32-bitinteger or single-precision floating-point (SPFP) values that are 32-bitin size. For word sized sources, the accumulator uses 64-bit integer ordouble-precision floating-point (DPFP) values that are 64-bit in size insome embodiments.

For an accumulator having 8× input sizes (in other words, theaccumulator input value is eight times the size of the packed dataelement sizes of the sources), table 1105 illustrates a configuration.For byte sized sources, the accumulator uses 64-bit integer.

As hinted at earlier, matrix operations circuitry may be included in acore, or as an external accelerator. FIG. 12 illustrates an embodimentof a system utilizing matrix operations circuitry. In this illustration,multiple entities are coupled with a ring interconnect 1245.

A plurality of cores, core 0 1201, core 1 1203, core 2 1205, and core N1207 provide non-tile-based instruction support. In some embodiments,matrix operations circuitry 1251 is provided in a core 1203, and inother embodiments matrix operations circuitry 1211 and 1213 areaccessible on the ring interconnect 1245.

Additionally, one or more memory controllers 1223-1225 are provided tocommunicate with memory 1233 and 1231 on behalf of the cores and/ormatrix operations circuitry.

FIG. 13 illustrates an embodiment of a processor core pipelinesupporting matrix operations using tiles. Branch prediction and decodecircuitry 1303 performs branch predicting of instructions, decoding ofinstructions, and/or both from instructions stored in instructionstorage 1301. For example, instructions detailed herein may be stored ininstruction storage. In some implementations, separate circuitry is usedfor branch prediction and in some embodiments, at least someinstructions are decoded into one or more micro-operations, micro-codeentry points, microinstructions, other instructions, or other controlsignals using microcode 1305. The branch prediction and decode circuitry1303 may be implemented using various different mechanisms. Examples ofsuitable mechanisms include, but are not limited to, look-up tables,hardware implementations, programmable logic arrays (PLAs), microcoderead only memories (ROMs), etc.

The branch prediction and decode circuitry 1303 is coupled toallocate/rename 1307 circuitry which is coupled, in some embodiments, toscheduler circuitry 1309. In some embodiments, these circuits provideregister renaming, register allocation, and/or scheduling functionalityby performing one or more of: 1) renaming logical operand values tophysical operand values (e.g., a register alias table in someembodiments), 2) allocating status bits and flags to the decodedinstruction, and 3) scheduling the decoded instruction for execution onexecution circuitry out of an instruction pool (e.g., using areservation station in some embodiments).

The scheduler circuitry 1309 represents any number of differentschedulers, including reservations stations, central instruction window,etc. The scheduler circuitry 1309 is coupled to, or includes, physicalregister file(s) 1315. Each of the physical register file(s) 1315represents one or more physical register files, different ones of whichstore one or more different data types, such as scalar integer, scalarfloating point, packed integer, packed floating point, vector integer,vector floating point, status (e.g., an instruction pointer that is theaddress of the next instruction to be executed), tiles, etc. In oneembodiment, the physical register file(s) 1315 comprises vectorregisters circuitry, write mask registers circuitry, and scalarregisters circuitry. These register circuits may provide architecturalvector registers, vector mask registers, and general-purpose registers.The physical register file(s) 1315 is overlapped by a retirement circuit1317 to illustrate various ways in which register renaming andout-of-order execution may be implemented (e.g., using a reorderbuffer(s) and a retirement register file(s); using a future file(s), ahistory buffer(s), and a retirement register file(s); using a registermaps and a pool of registers; etc.). The retirement circuit 1317 and thephysical register file(s) 1315 are coupled to the execution circuitry1311.

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor may also include separate instruction and data cache units anda shared L2 cache unit, alternative embodiments may have a singleinternal cache for both instructions and data, such as, for example, aLevel 1 (L1) internal cache, or multiple levels of internal cache. Insome embodiments, the system may include a combination of an internalcache and an external cache that is external to the core and/or theprocessor. Alternatively, all of the cache may be external to the coreand/or the processor.

The execution circuitry 1311 is a set of one or more execution circuits,including scalar circuitry 1321, vector/SIMD circuitry 1323, and matrixoperations circuitry 1327, as well as memory access circuitry 1325 toaccess cache 1313. The execution circuits perform various operations(e.g., shifts, addition, subtraction, multiplication) and on varioustypes of data (e.g., scalar floating point, packed integer, packedfloating point, vector integer, vector floating point). While someembodiments may include a number of execution units dedicated tospecific functions or sets of functions, other embodiments may includeonly one execution unit or multiple execution units that all perform allfunctions. The scalar circuitry 1321 performs scalar operations, thevector/SIMD circuitry 1323 performs vector/SIMD operations, and matrixoperations circuitry 1327 performs matrix (tile) operations detailedherein.

By way of example, the exemplary register renaming, out-of-orderissue/execution core architecture may implement a pipeline asfollows: 1) an instruction fetch circuit performs fetch and lengthdecoding stages; 2) the branch and decode circuitry 1303 performs adecode stage; 3) the allocate/rename 1307 circuitry performs anallocation stage and renaming stage; 4) the scheduler circuitry 1309performs a schedule stage; 5) physical register file(s) (coupled to, orincluded in, the scheduler circuitry 1309 and allocate/rename 1307circuitry and a memory unit perform a register read/memory read stage;the execution circuitry 1311 performs an execute stage; 6) a memory unitand the physical register file(s) unit(s) perform a write back/memorywrite stage; 7) various units may be involved in the exception handlingstage; and 8) a retirement unit and the physical register file(s)unit(s) perform a commit stage.

The core may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.), including theinstruction(s) described herein. In one embodiment, the core 1390includes logic to support a packed data instruction set extension (e.g.,AVX1, AVX2), thereby allowing the operations used by many multimediaapplications to be performed using packed data.

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads), and maydo so in a variety of ways including time sliced multithreading,simultaneous multithreading (where a single physical core provides alogical core for each of the threads that physical core issimultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

FIG. 14 illustrates an embodiment of a processor core pipelinesupporting matrix operations using tiles. Branch prediction and decodecircuitry 1403 performs branch predicting of instructions, decoding ofinstructions, and/or both from instructions stored in instructionstorage 1401. For example, instructions detailed herein may be stored ininstruction storage. In some implementations, separate circuitry is usedfor branch prediction and in some embodiments, at least someinstructions are decoded into one or more micro-operations, micro-codeentry points, microinstructions, other instructions, or other controlsignals using microcode 1405. The branch prediction and decode circuitry1403 may be implemented using various different mechanisms. Examples ofsuitable mechanisms include, but are not limited to, look-up tables,hardware implementations, programmable logic arrays (PLAs), microcoderead only memories (ROMs), etc.

The branch prediction and decode circuitry 1403 is coupled toallocate/rename 1407 circuitry which is coupled, in some embodiments, toscheduler circuitry 1409. In some embodiments, these circuits provideregister renaming, register allocation, and/or scheduling functionalityby performing one or more of: 1) renaming logical operand values tophysical operand values (e.g., a register alias table in someembodiments), 2) allocating status bits and flags to the decodedinstruction, and 3) scheduling the decoded instruction for execution onexecution circuitry out of an instruction pool (e.g., using areservation station in some embodiments).

The scheduler circuitry 1409 represents any number of differentschedulers, including reservations stations, central instruction window,etc. The scheduler unit(s) scheduler circuitry 1409 is coupled to, orincludes, physical register file(s) 1415. Each of the physical registerfile(s) 1415 represents one or more physical register files, differentones of which store one or more different data types, such as scalarinteger, scalar floating point, packed integer, packed floating point,vector integer, vector floating point, status (e.g., an instructionpointer that is the address of the next instruction to be executed),tiles, etc. In one embodiment, the physical register file(s) 1415comprises vector registers circuitry, write mask registers circuitry,and scalar registers circuitry. These register circuits may providearchitectural vector registers, vector mask registers, andgeneral-purpose registers. The physical register file(s) 1415 isoverlapped by a retirement circuit 1417 to illustrate various ways inwhich register renaming and out-of-order execution may be implemented(e.g., using a reorder buffer(s) and a retirement register file(s);using a future file(s), a history buffer(s), and a retirement registerfile(s); using a register maps and a pool of registers; etc.). Theretirement circuit 1417 and the physical register file(s) 1415 arecoupled to the execution circuitry 1411.

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor may also include separate instruction and data cache units anda shared L2 cache unit, alternative embodiments may have a singleinternal cache for both instructions and data, such as, for example, aLevel 1 (L1) internal cache, or multiple levels of internal cache. Insome embodiments, the system may include a combination of an internalcache and an external cache that is external to the core and/or theprocessor. Alternatively, all of the cache may be external to the coreand/or the processor.

The execution circuitry 1411 a set of one or more execution circuits1427 and a set of one or more memory access circuits 1425 to accesscache 1413. The execution circuits 1427 perform matrix (tile) operationsdetailed herein.

By way of example, the exemplary register renaming, out-of-orderissue/execution core architecture may implement a pipeline asfollows: 1) an instruction fetch circuit performs fetch and lengthdecoding stages; 2) the branch and decode circuitry 1403 performs adecode stage; 3) the allocate/rename 1407 circuitry performs anallocation stage and renaming stage; 4) the scheduler circuitry 1409performs a schedule stage; 5) physical register file(s) (coupled to, orincluded in, the scheduler circuitry 1409 and allocate/rename 1407circuitry and a memory unit perform a register read/memory read stage;the execution circuitry 1411 performs an execute stage; 6) a memory unitand the physical register file(s) unit(s) perform a write back/memorywrite stage; 7) various units may be involved in the exception handlingstage; and 8) a retirement unit and the physical register file(s)unit(s) perform a commit stage.

The core may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.), including theinstruction(s) described herein. In one embodiment, the core 1490includes logic to support a packed data instruction set extension (e.g.,AVX1, AVX2), thereby allowing the operations used by many multimediaapplications to be performed using packed data.

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads), and maydo so in a variety of ways including time sliced multithreading,simultaneous multithreading (where a single physical core provides alogical core for each of the threads that physical core issimultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

Layout

Throughout this description, data is expressed using row major datalayout. Column major users should translate the terms according to theirorientation. FIG. 15 illustrates an example of a matrix expressed in rowmajor format and column major format. As shown, matrix A is a 2×3matrix. When this matrix is stored in row major format, the dataelements of a row are consecutive. When this matrix is stored in columnmajor format, the data elements of a column are consecutive. It is awell-known property of matrices that A^(T)*B^(T)=(BA)^(T); wheresuperscript T means transform. Reading column major data as row majordata results in the matrix looking like the transform matrix.

In some embodiments, row-major semantics are utilized in hardware, andcolumn major data is to swap the operand order with the result beingtransforms of matrix, but for subsequent column-major reads from memoryit is the correct, non-transformed matrix.

For example, if there are two column-major matrices to multiply:

$\begin{matrix}{ab} & {gik} & {{ag} + {{bh}\mspace{14mu}{ai}} + {{bj}\mspace{14mu}{ak}} + {bl}} \\{c\; d^{*}} & {{hjl} =} & {{cg} + {{dh}\mspace{14mu}{ci}} + {{dj}\mspace{14mu}{ck}} + {dl}} \\{ef} & \; & {{eg} + {{fh}\mspace{14mu}{ei}} + {{fj}\mspace{14mu}{ek}} + {fl}} \\\left( {3 \times 2} \right) & \left( {2 \times 3} \right) & \left( {3 \times 3} \right)\end{matrix}$

The input matrices would be stored in linear memory (column-major) as:

a c e b d f

and

g h i j k l.

Reading those matrices as row-major with dimensions 2×3 and 3×2, theywould appear as:

$\begin{matrix}{a\mspace{14mu} c\mspace{14mu} e} & {and} & {g\mspace{14mu} h} \\{b\mspace{14mu} d\mspace{14mu} f} & \; & {i\mspace{11mu} j} \\{k\mspace{14mu} l} & \; & \;\end{matrix}$

Swapping the order and matrix multiplying:

$\begin{matrix}{g\mspace{11mu} h} & \; & {a\mspace{11mu} c\mspace{11mu} e} & {{ag} + {{bh}\mspace{14mu}{cg}} + {{dh}\mspace{14mu}{eg}} + {fh}} \\{i\mspace{11mu} j} & * & {{b\mspace{11mu} d\mspace{11mu} f} =} & {{ai} + {{bj}\mspace{14mu}{ci}} + {{dj}\mspace{14mu}{ei}} + {fj}} \\{k\mspace{11mu} l} & \; & \; & {{ak} + {{bl}\mspace{14mu}{ck}} + {{dl}\mspace{14mu}{ek}} + {fl}}\end{matrix}$

The transform matrix is out and can then be stored in in row-majororder:

ag + bh  cg + dh  eg + fh  ai + bj  ci + dj  ei + fj  ak + bl  ck + dl  ek + fl

and used in subsequent column major computations, it is the correctun-transformed matrix:

$\begin{matrix}{{ag} + {bh}} & {{ai} + {bj}} & {{ak} + {bl}} \\{{cg} + {dh}} & {{ci} + {dj}} & {{ck} + {dl}} \\{{eg} + {fh}} & {{ci} + {fj}} & {{ek} + {fl}}\end{matrix}$

Exemplary Usage

FIG. 16 illustrates an example of usage of matrices (tiles). In thisexample, matrix C 1601 includes two tiles, matrix A 1603 includes onetile, and matrix B 1605 includes two tiles. This figure shows an exampleof the inner loop of an algorithm to compute a matrix multiplication. Inthis example, two result tiles, tmm0 and tmm1, from matrix C 1601 areused to accumulate the intermediate results. One tile from the matrix A1603 (tmm2) is reused twice as it multiplied by two tiles from matrix B1605. Pointers to load a new A matrix (tile) and two new B matrices(tiles) from the directions indicated by the arrows. An outer loop, notshown, adjusts the pointers for the C tiles.

The exemplary code as shown includes the usage of a tile configurationinstruction and is executed to configure tile usage, load tiles, a loopto process the tiles, store tiles to memory, and release tile usage.

FIG. 17 illustrates an embodiment of usage of matrices (tiles). At 1701,tile usage is configured. For example, a TILECONFIG instruction isexecuted to configure tile usage including setting a number of rows andcolumns per tile. Typically, at least one matrix (tile) is loaded frommemory at 1703. At least one matrix (tile) operation is performed at1705 using the matrices (tiles). At 1707, at least one matrix (tile) isstored out to memory and a context switch can occur at 1709.

Exemplary Configuration

Tile Configuration Hardware Support

As discussed above, tile usage typically needs to be configured prior touse. For example, full usage of all rows and columns may not be needed.Not only does not configuring these rows and columns save power in someembodiments, but the configuration may be used to determine if anoperation will generate an error. For example, a matrix multiplicationof the form (N×M)*(L×N) will typically not work if M and L are not thesame.

Prior to using matrices using tiles, in some embodiments, tile supportis to be configured. For example, how many rows and columns per tile,tiles that are to be used, etc. are configured. A TILECONFIG instructionis an improvement to a computer itself as it provides for support toconfigure the computer to use a matrix accelerator (either as a part ofa processor core, or as an external device). In particular, an executionof the TILECONFIG instruction causes a configuration to be retrievedfrom memory and applied to matrix (tile) settings within a matrixaccelerator.

Tile Usage Configuration

FIG. 18 illustrates support for configuration of the usage of tilesaccording to an embodiment. A memory 1801 contains the tile description1803 of the matrices (tiles) to be supported.

Instruction execution resources 1811 of a processor/core 1805 storesaspects of a tile description 1803 into tile configurations 1817. Thetile configurations 1817 include palette table 1813 to detail what tilesfor a palette are configured (the number of rows and columns in eachtile) and a marking that matrix support is in use. In particular,instruction execution resources 1811 are configured to use tiles asspecified by the tile configurations 1817. The instruction executionresources 1811 may also include a machine specific register orconfiguration register to indicate tile usage. Additional values such asin-use and start values are also set. The tile configurations 1817utilize register(s) 1819 to store tile usage and configurationinformation.

FIG. 19 illustrates an embodiment of a description of the matrices(tiles) to be supported. This is the description that is to be storedupon an execution of a STTILECFG instruction. In this example, eachfield is a byte. In byte [0], a palette ID 1901 is stored. The paletteID is used to index a palette table 1813 which stores, per palette ID, anumber of bytes in a tile, and bytes per row of the tiles that areassociated with this ID as defined by the configuration.

Byte 1 stores a value to be stored in a “startRow” register 1903 andbyte 2 stores a value to be stored in a register, startP 1905. Tosupport restarting instructions after these events, the instructionsstore information these registers. To support restarting instructionsafter break events such as those detailed above, the instructions storeinformation in these registers. The startRow value indicates the rowthat should be used for restart. The startP value indicates the positionwithin the row for store operations when pairs are used and, in someembodiments, indicates the lower half of the row (in the lower tile of apair) or higher half of the row (in the higher tile of a pair).Generally, the position in the row (the column) is not needed.

With the exception of TILECONFIG and STTILECFG, successfully executingmatrix (tile) instructions will set both startRow and startP to zero.

Any time an interrupted matrix (tile) instruction is not restarted, itis the responsibility of software to zero the startRow and startPvalues. For example, unmasked floating point exception handlers mightdecide to finish the operation in software and change the programcounter value to another instruction, usually the next instruction. Inthis case the software exception handler must zero the startRow andstartP values in the exception presented to it by the operating systembefore resuming the program. The operating system will subsequentlyreload those values using a restore instruction.

Byte 3 stores an indication of pairs (1b per tile) of tiles 1907.

Bytes 16-17 store the number of rows 1913 and columns 1915 for tile 0,bytes 18-19 store the number of rows and columns for tile 1, etc. Inother words, each 2-byte group specifies a number of rows and columnsfor a tile. If a group of 2 bytes is not used to specify tileparameters, they should have the value zero. Specifying tile parametersfor more tiles than the implementation limit or the palette limitresults in a fault. Unconfigured tiles are set to an initial state with0 rows, 0 columns.

Finally, the configuration in memory typically ends with an endingdelineation such as all zeros for several consecutive bytes.

Exemplary Tile and Tile Configuration Storage

FIGS. 20(A)-(D) illustrate examples of register(s) 1819. FIG. 20(A)illustrates a plurality of registers 1819. As shown each tile (TMMO 2001. . . TMMN 2003) has a separate register with each register storing arow and column size for that particular tile. StartP 2011 and StartRow2013 are stored in separate registers. One or more status registers 2015are set (e.g., TILES_CONFIGURED=1) to indicate tiles are configured foruse.

FIG. 20(B) illustrates a plurality of registers 1819. As shown each tilehas separate registers for its rows and columns. For example, TMMO rowsconfiguration 2021, TMMO columns configuration 2023, StartP 2011 andStartRow 2013 are stored in separate registers. One or more statusregisters 2015 are set (e.g., TILES_CONFIGURED=1) to indicate tiles areconfigured for use.

FIG. 20(C) illustrates a single register 1819. As shown, this registerstores tile configurations (rows and columns per tile) 2031, StartP2011, and StartRow 2013 are stored in single register as packed dataregisters. One or more status registers 2015 are set (e.g.,TILES_CONFIGURED=1) to indicate tiles are configured for use.

FIG. 20(D) illustrates a plurality of registers 1819. As shown, a singleregister stores tile configuration (rows and columns per tile) 2031.StartP and StartRow are stored in separate registers 2011 and 2013. Oneor more status registers 2015 are set (e.g., TILES_CONFIGURED=1) toindicate tiles are configured for use.

Other combinations are contemplated such as combining the startregisters into a single register where they are shown separately, etc.

TILENZFMA/TILENZMUL Instructions

As mentioned above, today's common hardware accelerators for neuralnetworks perform mainly matrix multiplication in a dense format, nottaking into account the fact that there is a large percentage of zerosor close-to-zero values found in one (or both) matrices. This introducesinefficient usage of hardware resources (wasting power and computecycles to generate inconsequential products). Disclosed herein areembodiments of systems, processors, and methods to accelerate executionof matrix operations for use in machine learning and neural networks,including for training and inference. Disclosed embodiments improveperformance and reduce power consumption of multiplications in a neuralnetwork context.

In particular, disclosed embodiments propose a family of instructions todetect and avoid matrix operations that would produce zero-valued ornear-zero-valued (i.e., negligible or inconsequential) results. Dataelements that are candidates for being skipped are either to bedynamically detected, or to be tagged ahead-of-time with a fieldindicating as much.

Alternate, inferior approaches to hardware acceleration for neuralnetworks perform matrix multiplication assuming densely formattedmatrices and fail to take into account that there is often a largepercentage of zeros or close-to-zero (also referred to as “near-zero” or“approximately zero”) values found in one (or both) source matrices.This introduces inefficient usage of hardware resources because powerand execution resources are wasted.

Some alternate, inferior approaches require source matrices to haveparticular characteristics, namely the source matrices are sometimesassumed to be either sparse or dense. Disclosed embodiments, incontrast, do not force such assumptions.

Some alternate, inferior approaches fail to take advantage of near-zerovalues to extract more performance and power-saving. In contrast,disclosed embodiments detect data values that are close to zero (i.e.,as compared to a programmable threshold) and force those values to zeroin order to allow them to be skipped and enhance performance and powerconservation.

Hardware accelerators for neural networks, including for training andinference, strive to achieve the best raw performance numbers and powerto performance ratio values. Exploiting the native and injected sparsityin those neural networks is one way to help achieve these goals.

The proposed family of instructions is to avoid processing zero-valuedelements or elements that would produce negligible multiplicationresults. Additional terms used herein to refer to “negligible” resultsinclude “inconsequential” and “trivial.” Elements to be skipped are tobe identified dynamically or else tagged ahead-of-time.

Embodiments of a processor to execute matrix (tile) non-zero fusedmultiply-accumulate (TILENZFMA) instructions are to include decodecircuitry to decode an instruction having fields to specify an opcodeand locations of first source, second source, and destination matrices,the opcode indicating that the processor is to multiply each element atrow M and column K of the first source matrix with a correspondingelement at row K and column N of the second source matrix, andaccumulate a resulting product with previous contents of a correspondingelement at row M and column N of the destination matrix, the processorto skip multiplications that, based on detected values of thecorresponding multiplicands, would generate inconsequential results.Such processors further include scheduling circuitry to scheduleexecution of the instruction, and execution circuitry to execute theinstruction as per the opcode.

Embodiments of a processor to perform matrix (tile) non-zeromultiplication (TILENZMUL) instructions are to include: decode circuitryto decode an instruction having fields to specify an opcode andlocations of first source, second source, and destination matrices, theopcode indicating the processor, for each pair of corresponding elementsof the source matrices, is to generate a result of a multiplication andstore the result to a corresponding element of the destination matrix,while avoiding operations that, based on detected values of thecorresponding elements, would generate inconsequential results. Theprocessor is further to include scheduling circuitry to scheduleexecution of the instructions, and execution circuitry to execute theinstructions as per the opcode.

Exemplary Execution

FIG. 21A is a block diagram illustrating execution of a matrix non-zerofused multiply-accumulate (TILENZFMA) instruction, according to someembodiments. As shown, system 2100 is to execute TILENZFMA instruction2101, which includes fields to specify first source, second source, anddestination matrices, and an opcode indicating that the processor is tomultiply each element at row M and column K of the first source matrixwith a corresponding element at row K and column N of the second sourcematrix, and accumulate a resulting product with previous contents of acorresponding element at row M and column N of the destination matrix.The processor is to skip multiplications that, based on detected valuesof the corresponding multiplicands, would generate inconsequentialresults. In operation, decode circuitry (not shown) is to decodeTILENZFMA instruction 2101. The processor is further to use schedulingcircuitry (not shown) to schedule execution of the instruction, andexecution circuitry 2106 to execute the instructions as per the opcode.Advantageously, as shown, detecting one zero-valued element 2108, 2109in each of the two source matrices 2102, 2104 allows the processor toskip eight inconsequential multiplications.

Of course, the illustration of FIG. 21A is simplified to convey thepoint. It is expected that, in reality, the processor may operate onmuch larger matrices, such as 32×32 or larger matrices, and that atleast one of the source matrices will have much larger sparsity, such asincidence of zeros and near-zero values being between, say, 40% and 90%.

Execution of the TILENZFMA instruction according to disclosedembodiments is further illustrated and described with respect to FIGS.21C-E, 22A-B, 23, 28A-B, and 29A-B. A format of the TILENZFMAinstruction is further illustrated and described with respect to FIGS.24, 25A-B, and 26A-D.

FIG. 21B is a block diagram illustrating execution of a matrix (tile)non-zero multiplication (TILENZMUL) instruction according to someembodiments. Shown is an example of an element-by-element multiplicationof two source matrices. Here, system 2150 is to execute TILENZMULinstruction 2151, which includes fields to specify first source, secondsource, and destination matrices, and an opcode indicating that theprocessor is to multiply each element at row M and column N of the firstsource matrix with a corresponding element at row M and column N of thesecond source matrix, and accumulate a resulting product with previouscontents of a corresponding element at row M and column N of thedestination matrix, the processor to skip multiplications that, based ondetected values of the multiplicands, would generate inconsequentialresults (a.k.a., negligible results or trivial results). In operation,decode circuitry (not shown) is to decode TILENZMUL instruction 2151.The processor is further to use scheduling circuitry (not shown) toschedule execution of the instruction, and execution circuitry toexecute the instructions as per the opcode. Advantageously, as shown,the processor is to detect two zero-valued elements 2158 and 2159 infirst source matrix 2152 and second source matrix 2154, which allowavoidance of two inconsequential multiplications shown as zero values inthe destination matrix 2156.

In practical machine learning and matrix multiplication applications,the source matrices are expected to be much larger, for example, 16×16,and their sparsity is expected to be much greater, having, for example50% to 90% zero-valued elements.

Execution of the TILENZMUL instruction according to disclosedembodiments is further illustrated and described with respect to FIGS.21C-E, 22A-B, 28A-B, and 29A-B. A format of the TILENZMUL instruction isfurther illustrated and described with respect to FIGS. 24, 25A-B, and26A-D.

Identifying and Forcing Zero-Valued (Inconsequential) Data Elements

Disclosed embodiments of systems, circuits, and methods to perform thefamily of TILENZFMA and TILENZMUL instructions perform matrixmultiplication after first identifying and avoiding (e.g., skipping)multiplications that would yield inconsequential (e.g., negligible,trivial) results. For example, multiplying by zero-valued data elementsin either of the source matrices is avoided. But also, unlikealternative, inferior approaches, disclosed embodiments take advantageof values that are close to zero. This is to be done by forcing dataelements to zero if they are smaller than a programmable, predefinedthreshold. Of course, for signed data, their absolute value is comparedagainst the threshold.

When Optimization Candidates are to be Identified

Some embodiments of systems, circuits, and methods to perform the familyof TILENZFMA and TILENZMUL instructions detect candidates for providingoptimization opportunities dynamically, in the course of executing theinstruction. In such embodiments, execution of a multiplication ispreceded by checking whether a zero-valued or inconsequential productwould be generated, and, if so, skipping or avoiding performing themultiplication. Some such embodiments reduce power consumption bydisabling multiplication circuitry during the to-be-skipped cycle. Somesuch embodiments improve performance by scheduling a different, usefulmultiplication, such as a multiplication of a next pair of correspondingsource matrix elements, during the cycle that would otherwise beskipped. Either way, skipping a multiplication that would yield aninconsequential result improves the processor's power and performanceratio.

Some alternate embodiments of systems, circuits, and methods to performthe family of TILENZFMA and TILENZMUL instructions detect candidates forproviding optimization opportunities dynamically, but during ascheduling stage. In particular, such embodiments, when schedulingexecution of a multiplication, check whether a zero-valued orinconsequential product would be generated by a multiplication, and, ifso, skip (e.g., avoid) scheduling the multiplication. Instead, suchembodiments exploit the to-be-skipped cycle to gain performance orpower-saving advantage. Relatedly, some embodiments, after checking toavoid multiplications that would generate inconsequential results,maintain a queue or buffer of multiplications scheduled to be executed.

Yet further alternate embodiments of systems, circuits, and methods toperform the family of TILENZFMA and TILENZMUL instructions detect andtag source matrix elements to-be-skipped ahead of time. For example,some embodiments append a “skip-me” field (e.g., a bit) to each sourcematrix element that is to be skipped because using it as a multiplicandwould generate an inconsequential product. Such embodiments are to skip(avoid) multiplying with such tagged elements, and beneficially improvepower or performance during the cycle that would otherwise be used togenerate a negligible product. In some embodiments, such “skip-me”fields are to be appended to the source matrix data elements ahead oftime.

Similar embodiments of systems, circuits, and methods to perform thefamily of TILENZFMA and TILENZMUL instructions detect source matrixelements to-be-skipped ahead of time and specify a multi-bit non-zeromask having a bit for each element of a source matrix to be skipped. Insome embodiments, the bits of the NZ mask are mapped to the sourcematrix elements in row-major order. In some embodiments, the instructionis to specify a NZ mask for each of the first and second sourcematrices.

Identifying and Forcing Zero-Valued (Inconsequential) Data Elements

FIGS. 21C-E are block diagrams illustrating detection of inconsequentialmultiplication candidates in a source matrix, according to someembodiments.

FIG. 21C is a block diagram illustrating detection of near-zeromultiplicands, according to some embodiments. As shown, matrix 2162 is a4×4 matrix having four close-to-zero (near-zero) elements whose absolutevalues, equal to 1, are less than a threshold, 2. Also shown is a graph2164 plotting the elements of matrix 2162 in row-major order andidentifying the four elements whose absolute values are less than 2.Advantageously, disclosed embodiments are to skip those four elements,yielding better power-performance.

FIG. 21D is a block diagram illustrating detection of multiplicands neara constant value, according to some embodiments. As shown, matrix 2166is a 4×4 matrix whose elements have values huddled near a constantvalue, 10. Also shown is a graph 2168 plotting the elements of matrix2166 in row-major order and identifying the four elements whose absolutevalues are near the constant, here 10. To optimize performance,disclosed embodiments identify the four elements whose values are withina threshold, say 2, of the constant. Advantageously, some embodimentsadd a normalization transformation by subtracting the constant anddividing by the standard deviation around it to induce near-zero values.

FIG. 21E is a block diagram illustrating detection of multiplicands neara mean value, according to some embodiments. As shown, matrix 2170 is a4×4 matrix whose elements have values huddled near a mean, 12.7. Alsoshown is a graph 2172 plotting the elements of matrix 2170 in row-majororder and emphasizing the two elements whose absolute values are nearthe mean. Here again, some embodiments add a normalization thatsubtracts the mean from each value and divides by the standarddeviation, and results in a matrix that has significantly morezero-valued or near-zero values.

It should be noted that a constant against which source matrix elementsare to be compared can be selected using different statisticalcharacteristics, such as a median, a mode, a minimum, or a maximum, toname a few non-limiting examples.

Exemplary Execution Circuitry

FIG. 22A is a block diagram illustrating execution circuitry to performa matrix non-zero fused multiply-accumulate (TILENZFMA) instruction,according to some embodiments. As shown, system 2200 is to executeTILENZFMA instruction 2201, which includes fields to specify a firstsource matrix 2204 (having M rows by K columns), a second source matrix(having K rows by N columns) (not shown), a destination matrix (having Mrows by N columns) (not shown), and an opcode indicating that theprocessor is to multiply each element at row M and column K of the firstsource matrix 2204 with a corresponding element at row K and column N ofthe second source matrix, and accumulate a resulting product withprevious contents of a corresponding element at row M and column N ofthe destination matrix, the processor to skip multiplications that,based on detected values of the corresponding multiplicands, wouldgenerate inconsequential results.

Here, 4×4 source matrix 2204 has values to be compared against athreshold 2202 using execution circuitry 2206, including comparator2208, which is to output a signal to enable fused multiply-accumulate(FMA) circuit 2210. Threshold 2202 is to be specified in any of aninstruction field, as part of the instruction opcode, or programmedahead of time in a configuration register (e.g., TILECONFIG). As shown,threshold 2202 equals 2, which means that four of the sixteen elementsof the source matrix (tile) have absolute values that are less than thethreshold. In operation, when processing those four elements, theprocessor is to avoid enabling FMA 2210. In some cases, other usefulwork is scheduled to use FMA 2210 during such cycles in order to avoidwasting execution resources and improve performance.

Enabling or disabling FMA 2210 is performed in any of several ways,aiming to reduce power consumption. For example, in some embodiments,the clocks and/or supply voltage to the FMA are halted.

Execution of the TILENZFMA instructions according to disclosedembodiments is further illustrated and described with respect to FIGS.21A-E, 22B, 23, 28A-B, and 29A-B. A format of the TILENZFMA instructionsis further illustrated and described with respect to FIGS. 24, 25A-B,and 26A-D.

Reducing Loss-of-Accuracy when Forcing Zeroes

Executing the disclosed family of TILENZFMA and TILENZMUL instructionssometimes uses rounding to reduce the loss of accuracy caused by forcinginconsequential elements to zero.

FIG. 22B is a block diagram illustrating execution circuitry to performa matrix non-zero fused multiply-accumulate (TILENZFMA) instruction,according to some embodiments. As shown, system 2250 is to executeTILENZFMA instruction 2251, which includes fields to specify a threshold2252, a first source matrix 2254 (having M rows by K columns), a secondsource matrix (having K rows by N columns) (not shown), a destinationmatrix (having M rows by N columns) (not shown), and an opcodeindicating that the processor is to multiply each element at row M andcolumn K of first source matrix 2254 with a corresponding element at rowK and column N of the second source matrix, and accumulate a resultingproduct with previous contents of a corresponding element at row M andcolumn N of the destination matrix, the processor to skipmultiplications that, based on detected values of the correspondingmultiplicands, would generate inconsequential results.

Here, 4×4 source matrix 2254 has values to be compared against threshold2252 using execution circuitry 2256, including bitwise selector 2258,which is to route bits [31:N] to OR gate 2259, which is to output asignal to enable fused multiply-accumulate (FMA) circuit 2260. As shown,threshold 2252 is 2, which means that four of the sixteen elements ofthe source matrix (tile) have absolute values that are less than thethreshold. If OR gate 2259 detects a 1 in any of bits [31:N], then thedata element is larger than the threshold and the FMA 2260 is to beenabled.

Concurrently, bits [N−1:0] of each element are to be used as roundingcontrols (i.e., guard bits and sticky bits, as is known) to determinewhether to conditionally add a ‘1’ to the source element, in order toenhance the accuracy of the matrix multiplication when a MULtiplicationusing the element is to be skipped.

In some embodiments, the threshold is to be specified by the instruction2251. When processing those four elements, the processor is to avoidenabling FMA 2260. In some cases, other useful work is scheduled to useFMA 2260 during such cycles in order to avoid wasting executionresources.

Execution of the TILENZFMA instruction according to disclosedembodiments is further illustrated and described with respect to FIGS.21A-E, 22A, 23, 28A-B, and 29A-B. A format of the TILENZMUL andTILENZFMA instructions is illustrated and described with respect toFIGS. 24, 25A-B, and 26A-D.

Exemplary Matrix Compression

FIG. 22C is a block diagram illustrating an embodiment of matrixcompression. In some embodiments, TILENZMUL and TILENZFMA instructionsare further accelerated by compressing one or both of the first andsecond source matrices before the multiplication. As shown, block flowdiagram 2270 shows four uncompressed matrices (tiles 1-4) 2272,execution circuitry 2276, and the same four matrices (tiles 1-4) incompressed format 2278, with non-zero (NZ) bitmasks 2280. Compressedmatrices (tiles) 2278 offer a power and performance advantage both interms of memory movement and in use of execution resources becausecolumns 4-15, which contain only zero-valued elements, can be skippedaltogether, resulting in faster overall execution and lower overallpower consumption.

In operation, in some embodiments, one or both of the first matrix andthe second source matrix specified by a TILENZMUL or TILENZFMAinstruction are compressed ahead of time by either hardware (as shown)or software.

In some embodiments, one or both of the first and second source matricesis stored in compressed format in memory and decompressed when loadedfor use. Such an optimization improves memory utilization and bandwidth.

Exemplary Methods of Execution

FIG. 23 illustrates an embodiment of a processor executing a matrixnon-zero fused multiply-accumulate (TILENZFMA) instruction, according tosome embodiments. As shown, FIG. 23 illustrates an embodiment of aprocessor executing a flow 2300 to process a TILENZFMA instruction 2303.As shown, at 2305, the processor is to decode an instruction usingdecode circuitry, the instruction having fields to specify an opcode andlocations of first source, second source, and destination matrices, theopcode indicating that the processor is to multiply each element at rowM and column K of the first source matrix with a corresponding elementat row K and column N of the second source matrix, and accumulate aresulting product with previous contents of a corresponding element atrow M and column N of the destination matrix, the processor to skipmultiplications that, based on detected values of the correspondingmultiplicands, would generate inconsequential results.

Note that an embodiment of a processor flow for executing a matrix(tile) non-zero multiplication (TILENZMUL) instruction is similar toflow 2300, with the exception that the opcode included with theinstruction and decoded at operation 2305 opcode would indicate that theprocessor is to multiply each element at row M and column N of the firstsource matrix with a corresponding element at row M and column N of thesecond source matrix, and accumulate a resulting product with previouscontents of a corresponding element at row M and column N of thedestination matrix, the processor to skip multiplications that, based ondetected values of the multiplicands, would generate inconsequentialresults (a.k.a., negligible results or trivial results).

In some embodiments, at 2307 the processor is to schedule, usingscheduling circuitry, execution of the instruction. Operation 2307 isoptional (as indicated by its dashed border) insofar as it may occur ata different time, or not at all.

At 2309, the processor is to execute, using execution circuitry, theinstruction as per the opcode. Execution circuitry is furtherillustrated and described with respect to FIGS. 3-14. In someembodiments, execution circuitry is a matrix operations accelerator,such as that illustrated and described as accelerator 307 (FIG. 3). Insome embodiments, execution circuitry is a matrix operations circuit,such as matrix operations circuitry 405 (FIG. 4), 505 (FIG. 5), or 1213(FIG. 12), and 1327 (FIG. 13).

In some embodiments, the instruction is committed or retired at 2311,which is optional (as indicated by its dashed border) insofar as it mayoccur at a different time, or not at all.

Execution of the TILENZFMA and TILENAZMUL instructions according todisclosed embodiments is further illustrated and described with respectto FIGS. 21A-E, 22A-B, 28A-B, and 29A-B. A format of the TILENZFMA andTILENAZMUL instructions is further illustrated and described withrespect to FIGS. 24, 25A-B, and 26A-D.

Exemplary Formats of TILENZFMA and TILENZMUL Instructions

FIG. 24 is a block diagram illustrating a format of matrix non-zerofused multiply-accumulate (TILENZFMA) and matrix non-zero multiply(TILENZMUL) instructions, according to some embodiments. As shown,TILENZFMA and TILENZMUL instruction format 2400 includes fields forspecifying an opcode 2402 (either TILENZFMA or TILENZMUL) and locationsof first source 2406, second source 2408, and destination 2404 matrices.

In the case of a TILENZFMA instruction, the opcode is to indicate thatthe processor is to multiply each element at row M and column K of thefirst source matrix with a corresponding element at row K and column Nof the second source matrix, and accumulate a resulting product withprevious contents of a corresponding element at row M and column N ofthe destination matrix, the processor to skip multiplications that,based on detected values of the corresponding multiplicands, wouldgenerate inconsequential results.

In the case of a TILENZMUL instruction, opcode 2402 is to indicate thatthe processor is to multiply each element at row M and column N of thefirst source matrix with a corresponding element at row M and column Nof the second source matrix, and accumulate a resulting product withprevious contents of a corresponding element at row M and column N ofthe destination matrix, the processor to skip multiplications that,based on detected values of the corresponding multiplicands, wouldgenerate inconsequential results.

The source and destination matrices may be located in memory, in acollection of register file registers, and in a collection of tileregisters, as specified by instruction 2400.

In some embodiments, instruction 2400 further specifies threshold 2410,which is to serve as a threshold below which element values provideinconsequential products. The use of a threshold by disclosedembodiments is further illustrated and described with respect to FIGS.21A-E, 22A-B, and 23. Threshold 2410 is optional, as indicated by itsdashed outline, insofar at the threshold 2410 is sometimes provided aspart of the opcode 2402, or by a configuration register programmed aheadof time by software.

In some embodiments, instruction 2400 further specifies NZ mask 2412,which is a multibit value, with each bit to specify whether acorresponding element is zero-valued or not.

TILENZFMA/TILENZMUL instruction 2400 further includes several optionalparameters to control the processor's behavior, including M 2414, K2416, N 2418, element size 2420 (nibble, byte, word, doubleword, orquadword), element format 2422 (integer, single-precision floatingpoint, double-precision floating point).

In some embodiments, instruction 2400 specifies writemask 2422(multi-bit value with one bit per destination element, the bit tocontrol whether the destination element is to be updated, or if it is tobe zeroed or merged).

Opcode 2402 is shown including an asterisk, which is to convey thatadditional prefixes and/or suffixes may be added to specify instructionbehavior. One or more of instructions modifiers 2410, 2412, 2414, 2416,2418, 2420, 2422, and 2424, in some embodiments are specified usingprefixes or suffixes to opcode 2402.

In some embodiments, one or more of optional instructions modifiers2410, 2412, 2414, 2416, 2418, 2420, 2422, and 2424 are encoded in animmediate field (not shown) optionally included with the instruction2400. In some embodiments, one or more of optional instructionsmodifiers 2410, 2412, 2414, 2416, 2418, 2420, 2422, and 2424 isspecified via a configuration/status register (e.g., XTILECONFIG). Inother words, when any one or more of optional modifiers 2410, 2412,2414, 2416, 2418, 2420, 2422, and 2424 are not specified by theinstruction, they sometimes use implicit parameters that are inheritedfrom other parts of the tile architecture.

Detailed Exemplary Systems, Processors, and Emulation

Detailed herein are examples of hardware, software, etc. to execute theabove described instructions. For example, what is described belowdetails aspects of instruction execution including various pipelinestages such as fetch, decode, schedule, execute, retire, etc.

Instruction Sets

An instruction set may include one or more instruction formats. A giveninstruction format may define various fields (e.g., number of bits,location of bits) to specify, among other things, the operation to beperformed (e.g., opcode) and the operand(s) on which that operation isto be performed and/or other data field(s) (e.g., mask). Someinstruction formats are further broken down though the definition ofinstruction templates (or subformats). For example, the instructiontemplates of a given instruction format may be defined to have differentsubsets of the instruction format's fields (the included fields aretypically in the same order, but at least some have different bitpositions because there are less fields included) and/or defined to havea given field interpreted differently. Thus, each instruction of an ISAis expressed using a given instruction format (and, if defined, in agiven one of the instruction templates of that instruction format) andincludes fields for specifying the operation and the operands. Forexample, an exemplary ADD instruction has a specific opcode and aninstruction format that includes an opcode field to specify that opcodeand operand fields to select operands (source1/destination and source2);and an occurrence of this ADD instruction in an instruction stream willhave specific contents in the operand fields that select specificoperands. A set of SIMD extensions referred to as the Advanced VectorExtensions (AVX) (AVX1 and AVX2) and using the Vector Extensions (VEX)coding scheme has been released and/or published (e.g., see Intel® 64and IA-32 Architectures Software Developer's Manual, September 2014; andsee Intel® Advanced Vector Extensions Programming Reference, October2014).

Exemplary Instruction Formats

Embodiments of the instruction(s) described herein may be embodied indifferent formats. Additionally, exemplary systems, architectures, andpipelines are detailed below. Embodiments of the instruction(s) may beexecuted on such systems, architectures, and pipelines, but are notlimited to those detailed.

Generic Vector Friendly Instruction Format

A vector friendly instruction format is an instruction format that issuited for vector instructions (e.g., there are certain fields specificto vector operations). While embodiments are described in which bothvector and scalar operations are supported through the vector friendlyinstruction format, alternative embodiments use only vector operationsthe vector friendly instruction format.

FIGS. 25A-25B are block diagrams illustrating a generic vector friendlyinstruction format and instruction templates thereof according toembodiments. FIG. 25A is a block diagram illustrating a generic vectorfriendly instruction format and class A instruction templates thereofaccording to embodiments; while FIG. 25B is a block diagram illustratingthe generic vector friendly instruction format and class B instructiontemplates thereof according to embodiments. Specifically, a genericvector friendly instruction format 2500 for which are defined class Aand class B instruction templates, both of which include no memoryaccess 2505 instruction templates and memory access 2520 instructiontemplates. The term generic in the context of the vector friendlyinstruction format refers to the instruction format not being tied toany specific instruction set.

While embodiments will be described in which the vector friendlyinstruction format supports the following: a 64 byte vector operandlength (or size) with 32 bit (4 byte) or 64 bit (8 byte) data elementwidths (or sizes) (and thus, a 64 byte vector consists of either 16doubleword-size elements or alternatively, 8 quadword-size elements); a64 byte vector operand length (or size) with 16 bit (2 byte) or 8 bit (1byte) data element widths (or sizes); a 32 byte vector operand length(or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit (2 byte), or 8bit (1 byte) data element widths (or sizes); and a 16 byte vectoroperand length (or size) with 32 bit (4 byte), 64 bit (8 byte), 16 bit(2 byte), or 8 bit (1 byte) data element widths (or sizes); alternativeembodiments may support more, less and/or different vector operand sizes(e.g., 256 byte vector operands) with more, less, or different dataelement widths (e.g., 128 bit (16 byte) data element widths).

The class A instruction templates in FIG. 25A include: 1) within the nomemory access 2505 instruction templates there is shown a no memoryaccess, full round control type operation 2510 instruction template anda no memory access, data transform type operation 2515 instructiontemplate; and 2) within the memory access 2520 instruction templatesthere is shown a memory access, temporal 2525 instruction template and amemory access, non-temporal 2530 instruction template. The class Binstruction templates in FIG. 25B include: 1) within the no memoryaccess 2505 instruction templates there is shown a no memory access,write mask control, partial round control type operation 2512instruction template and a no memory access, write mask control, vsizetype operation 2517 instruction template; and 2) within the memoryaccess 2520 instruction templates there is shown a memory access, writemask control 2527 instruction template.

The generic vector friendly instruction format 2500 includes thefollowing fields listed below in the order illustrated in FIGS. 25A-25B.

Format field 2540—a specific value (an instruction format identifiervalue) in this field uniquely identifies the vector friendly instructionformat, and thus occurrences of instructions in the vector friendlyinstruction format in instruction streams. As such, this field isoptional in the sense that it is not needed for an instruction set thathas only the generic vector friendly instruction format.

Base operation field 2542—its content distinguishes different baseoperations.

Register index field 2544—its content, directly or through addressgeneration, specifies the locations of the source and destinationoperands, be they in registers or in memory. These include a sufficientnumber of bits to select N registers from a P×Q (e.g. 32×512, 16×128,32×1024, 64×1024) register file. While in one embodiment N may be up tothree sources and one destination register, alternative embodiments maysupport more or less sources and destination registers (e.g., maysupport up to two sources where one of these sources also acts as thedestination, may support up to three sources where one of these sourcesalso acts as the destination, may support up to two sources and onedestination).

Modifier field 2546—its content distinguishes occurrences ofinstructions in the generic vector instruction format that specifymemory access from those that do not; that is, between no memory access2505 instruction templates and memory access 2520 instruction templates.Memory access operations read and/or write to the memory hierarchy (insome cases specifying the source and/or destination addresses usingvalues in registers), while non-memory access operations do not (e.g.,the source and destinations are registers). While in one embodiment thisfield also selects between three different ways to perform memoryaddress calculations, alternative embodiments may support more, less, ordifferent ways to perform memory address calculations.

Augmentation operation field 2550—its content distinguishes which one ofa variety of different operations to be performed in addition to thebase operation. This field is context specific. In one embodiment, thisfield is divided into a class field 2568, an alpha field 2552, and abeta field 2554. The augmentation operation field 2550 allows commongroups of operations to be performed in a single instruction rather than2, 3, or 4 instructions.

Scale field 2560—its content allows for the scaling of the index field'scontent for memory address generation (e.g., for address generation thatuses 2^(scale)*index+base).

Displacement Field 2562A—its content is used as part of memory addressgeneration (e.g., for address generation that uses2^(scale)*index+base+displacement).

Displacement Factor Field 2562B (note that the juxtaposition ofdisplacement field 2562A directly over displacement factor field 2562Bindicates one or the other is used)—its content is used as part ofaddress generation; it specifies a displacement factor that is to bescaled by the size of a memory access (N)—where N is the number of bytesin the memory access (e.g., for address generation that uses2^(scale)*index+base+scaled displacement). Redundant low-order bits areignored and hence, the displacement factor field's content is multipliedby the memory operands total size (N) in order to generate the finaldisplacement to be used in calculating an effective address. The valueof N is determined by the processor hardware at runtime based on thefull opcode field 2574 (described later herein) and the datamanipulation field 2554C. The displacement field 2562A and thedisplacement factor field 2562B are optional in the sense that they arenot used for the no memory access 2505 instruction templates and/ordifferent embodiments may implement only one or none of the two.

Data element width field 2564—its content distinguishes which one of anumber of data element widths is to be used (in some embodiments for allinstructions; in other embodiments for only some of the instructions).This field is optional in the sense that it is not needed if only onedata element width is supported and/or data element widths are supportedusing some aspect of the opcodes.

Write mask field 2570—its content controls, on a per data elementposition basis, whether that data element position in the destinationvector operand reflects the result of the base operation andaugmentation operation. Class A instruction templates supportmerging-writemasking, while class B instruction templates support bothmerging- and zeroing-writemasking. When merging, vector masks allow anyset of elements in the destination to be protected from updates duringthe execution of any operation (specified by the base operation and theaugmentation operation); in other one embodiment, preserving the oldvalue of each element of the destination where the corresponding maskbit has a 0. In contrast, when zeroing vector masks allow any set ofelements in the destination to be zeroed during the execution of anyoperation (specified by the base operation and the augmentationoperation); in one embodiment, an element of the destination is set to 0when the corresponding mask bit has a 0 value. A subset of thisfunctionality is the ability to control the vector length of theoperation being performed (that is, the span of elements being modified,from the first to the last one); however, it is not necessary that theelements that are modified be consecutive. Thus, the write mask field2570 allows for partial vector operations, including loads, stores,arithmetic, logical, etc. While embodiments are described in which thewrite mask field's 2570 content selects one of a number of write maskregisters that contains the write mask to be used (and thus the writemask field's 2570 content indirectly identifies that masking to beperformed), alternative embodiments instead or additional allow the maskwrite field's 2570 content to directly specify the masking to beperformed.

Immediate field 2572—its content allows for the specification of animmediate. This field is optional in the sense that is it not present inan implementation of the generic vector friendly format that does notsupport immediate and it is not present in instructions that do not usean immediate.

Class field 2568—its content distinguishes between different classes ofinstructions. With reference to FIGS. 25A-B, the contents of this fieldselect between class A and class B instructions. In FIGS. 25A-B, roundedcorner squares are used to indicate a specific value is present in afield (e.g., class A 2568A and class B 2568B for the class field 2568respectively in FIGS. 25A-B).

Instruction Templates of Class A

In the case of the non-memory access 2505 instruction templates of classA, the alpha field 2552 is interpreted as an RS field 2552A, whosecontent distinguishes which one of the different augmentation operationtypes are to be performed (e.g., round 2552A.1 and data transform2552A.2 are respectively specified for the no memory access, round typeoperation 2510 and the no memory access, data transform type operation2515 instruction templates), while the beta field 2554 distinguisheswhich of the operations of the specified type is to be performed. In theno memory access 2505 instruction templates, the scale field 2560, thedisplacement field 2562A, and the displacement factor field 25626 arenot present.

No-Memory Access Instruction Templates—Full Round Control Type Operation

In the no memory access full round control type operation 2510instruction template, the beta field 2554 is interpreted as a roundcontrol field 2554A, whose content(s) provide static rounding. While inthe described embodiments the round control field 2554A includes asuppress all floating-point exceptions (SAE) field 2556 and a roundoperation control field 2558, alternative embodiments may support mayencode both these concepts into the same field or only have one or theother of these concepts/fields (e.g., may have only the round operationcontrol field 2558).

SAE field 2556—its content distinguishes whether or not to disable theexception event reporting; when the SAE field's 2556 content indicatessuppression is enabled, a given instruction does not report any kind offloating-point exception flag and does not raise any floating-pointexception handler.

Round operation control field 2558—its content distinguishes which oneof a group of rounding operations to perform (e.g., Round-up,Round-down, Round-towards-zero and Round-to-nearest). Thus, the roundoperation control field 2558 allows for the changing of the roundingmode on a per instruction basis. In one embodiment where a processorincludes a control register for specifying rounding modes, the roundoperation control field's 2550 content overrides that register value.

No Memory Access Instruction Templates—Data Transform Type Operation

In the no memory access data transform type operation 2515 instructiontemplate, the beta field 2554 is interpreted as a data transform field2554B, whose content distinguishes which one of a number of datatransforms is to be performed (e.g., no data transform, swizzle,broadcast).

In the case of a memory access 2520 instruction template of class A, thealpha field 2552 is interpreted as an eviction hint field 2552B, whosecontent distinguishes which one of the eviction hints is to be used (inFIG. 25A, temporal 2552B.1 and non-temporal 2552B.2 are respectivelyspecified for the memory access, temporal 2525 instruction template andthe memory access, non-temporal 2530 instruction template), while thebeta field 2554 is interpreted as a data manipulation field 2554C, whosecontent distinguishes which one of a number of data manipulationoperations (also known as primitives) is to be performed (e.g., nomanipulation; broadcast; up conversion of a source; and down conversionof a destination). The memory access 2520 instruction templates includethe scale field 2560, and optionally the displacement field 2562A or thedisplacement factor field 2562B.

Vector memory instructions perform vector loads from and vector storesto memory, with conversion support. As with regular vector instructions,vector memory instructions transfer data from/to memory in a dataelement-wise fashion, with the elements that are actually transferred isdictated by the contents of the vector mask that is selected as thewrite mask.

Memory Access Instruction Templates—Temporal

Temporal data is data likely to be reused soon enough to benefit fromcaching. This is, however, a hint, and different processors mayimplement it in different ways, including ignoring the hint entirely.

Memory Access Instruction Templates—Non-Temporal

Non-temporal data is data unlikely to be reused soon enough to benefitfrom caching in the 1st-level cache and should be given priority foreviction. This is, however, a hint, and different processors mayimplement it in different ways, including ignoring the hint entirely.

Instruction Templates of Class B

In the case of the instruction templates of class B, the alpha field2552 is interpreted as a write mask control (Z) field 2552C, whosecontent distinguishes whether the write masking controlled by the writemask field 2570 should be a merging or a zeroing.

In the case of the non-memory access 2505 instruction templates of classB, part of the beta field 2554 is interpreted as an RL field 2557A,whose content distinguishes which one of the different augmentationoperation types are to be performed (e.g., round 2557A.1 and vectorlength (VSIZE) 2557A.2 are respectively specified for the no memoryaccess, write mask control, partial round control type operation 2512instruction template and the no memory access, write mask control, VSIZEtype operation 2517 instruction template), while the rest of the betafield 2554 distinguishes which of the operations of the specified typeis to be performed. In the no memory access 2505 instruction templates,the scale field 2560, the displacement field 2562A, and the displacementfactor field 2562B are not present.

In the no memory access, write mask control, partial round control typeoperation 2510 instruction template, the rest of the beta field 2554 isinterpreted as a round operation field 2559A and exception eventreporting is disabled (a given instruction does not report any kind offloating-point exception flag and does not raise any floating-pointexception handler).

Round operation control field 2559A—just as round operation controlfield 2558, its content distinguishes which one of a group of roundingoperations to perform (e.g., Round-up, Round-down, Round-towards-zeroand Round-to-nearest). Thus, the round operation control field 2559Aallows for the changing of the rounding mode on a per instruction basis.In one embodiment where a processor includes a control register forspecifying rounding modes, the round operation control field's 2550content overrides that register value.

In the no memory access, write mask control, VSIZE type operation 2517instruction template, the rest of the beta field 2554 is interpreted asa vector length field 2559B, whose content distinguishes which one of anumber of data vector lengths is to be performed on (e.g., 128, 256, or512 byte).

In the case of a memory access 2520 instruction template of class B,part of the beta field 2554 is interpreted as a broadcast field 2557B,whose content distinguishes whether or not the broadcast type datamanipulation operation is to be performed, while the rest of the betafield 2554 is interpreted the vector length field 2559B. The memoryaccess 2520 instruction templates include the scale field 2560, andoptionally the displacement field 2562A or the displacement factor field2562B.

With regard to the generic vector friendly instruction format 2500, afull opcode field 2574 is shown including the format field 2540, thebase operation field 2542, and the data element width field 2564. Whileone embodiment is shown where the full opcode field 2574 includes all ofthese fields, the full opcode field 2574 includes less than all of thesefields in embodiments that do not support all of them. The full opcodefield 2574 provides the operation code (opcode).

The augmentation operation field 2550, the data element width field2564, and the write mask field 2570 allow these features to be specifiedon a per instruction basis in the generic vector friendly instructionformat.

The combination of write mask field and data element width field createtyped instructions in that they allow the mask to be applied based ondifferent data element widths.

The various instruction templates found within class A and class B arebeneficial in different situations. In some embodiments, differentprocessors or different cores within a processor may support only classA, only class B, or both classes. For instance, a high performancegeneral purpose out-of-order core intended for general-purpose computingmay support only class B, a core intended primarily for graphics and/orscientific (throughput) computing may support only class A, and a coreintended for both may support both (of course, a core that has some mixof templates and instructions from both classes but not all templatesand instructions from both classes is within the purview of theinvention). Also, a single processor may include multiple cores, all ofwhich support the same class or in which different cores supportdifferent class. For instance, in a processor with separate graphics andgeneral-purpose cores, one of the graphics cores intended primarily forgraphics and/or scientific computing may support only class A, while oneor more of the general-purpose cores may be high performance generalpurpose cores with out of order execution and register renaming intendedfor general-purpose computing that support only class B. Anotherprocessor that does not have a separate graphics core, may include onemore general purpose in-order or out-of-order cores that support bothclass A and class B. Of course, features from one class may also beimplement in the other class in different embodiments. Programs writtenin a high level language would be put (e.g., just in time compiled orstatically compiled) into an variety of different executable forms,including: 1) a form having only instructions of the class(es) supportedby the target processor for execution; or 2) a form having alternativeroutines written using different combinations of the instructions of allclasses and having control flow code that selects the routines toexecute based on the instructions supported by the processor which iscurrently executing the code.

Exemplary Specific Vector Friendly Instruction Format

FIG. 26A is a block diagram illustrating an exemplary specific vectorfriendly instruction format according to embodiments. FIG. 26A shows aspecific vector friendly instruction format 2600 that is specific in thesense that it specifies the location, size, interpretation, and order ofthe fields, as well as values for some of those fields. The specificvector friendly instruction format 2600 may be used to extend the x86instruction set, and thus some of the fields are similar or the same asthose used in the existing x86 instruction set and extension thereof(e.g., AVX). This format remains consistent with the prefix encodingfield, real opcode byte field, MOD R/M field, SIB field, displacementfield, and immediate fields of the existing x86 instruction set withextensions. The fields from FIG. 25A into which the fields from FIG. 26Amap are illustrated.

It should be understood that, although embodiments are described withreference to the specific vector friendly instruction format 2600 in thecontext of the generic vector friendly instruction format 2500 forillustrative purposes, the invention is not limited to the specificvector friendly instruction format 2600 except where claimed. Forexample, the generic vector friendly instruction format 2500contemplates a variety of possible sizes for the various fields, whilethe specific vector friendly instruction format 2600 is shown as havingfields of specific sizes. By way of specific example, while the dataelement width field 2564 is illustrated as a one-bit field in thespecific vector friendly instruction format 2600, the invention is notso limited (that is, the generic vector friendly instruction format 2500contemplates other sizes of the data element width field 2564).

The specific vector friendly instruction format 2600 includes thefollowing fields listed below in the order illustrated in FIG. 26A.

EVEX Prefix 2602 (Bytes 0-3)—is encoded in a four-byte form.

Format Field 2540 (EVEX Byte 0, bits [7:0])—the first byte (EVEX Byte 0)is the format field 2540 and it contains 0x62 (the unique value used fordistinguishing the vector friendly instruction format in oneembodiment).

The second-fourth bytes (EVEX Bytes 1-3) include a number of bit fieldsproviding specific capability.

REX field 2605 (EVEX Byte 1, bits [7-5])—consists of an EVEX.R bit field(EVEX Byte 1, bit [7]—R), EVEX.X bit field (EVEX byte 1, bit [6]—X), andEVEX.B bit field (EVEX Byte 1, bit [5]—B). The EVEX.R, EVEX.X, andEVEX.B bit fields provide the same functionality as the correspondingVEX bit fields, and are encoded using 1s complement form, i.e. ZMM0 isencoded as 1111B, ZMM15 is encoded as 0000B. Other fields of theinstructions encode the lower three bits of the register indexes as isknown in the art (rrr, xxx, and bbb), so that Rrrr, Xxxx, and Bbbb maybe formed by adding EVEX.R, EVEX.X, and EVEX.B.

REX′ field 2510—this is the first part of the REX′ field 2510 and is theEVEX.R′ bit field (EVEX Byte 1, bit [4]—R′) that is used to encodeeither the upper 16 or lower 16 of the extended 32 register set. In oneembodiment, this bit, along with others as indicated below, is stored inbit inverted format to distinguish (in the well-known x86 32-bit mode)from the BOUND instruction, whose real opcode byte is 62, but does notaccept in the MOD R/M field (described below) the value of 11 in the MODfield; alternative embodiments do not store this and the other indicatedbits below in the inverted format. A value of 1 is used to encode thelower 16 registers. In other words, R′Rrrr is formed by combiningEVEX.R′, EVEX.R, and the other RRR from other fields.

Opcode map field 2615 (EVEX byte 1, bits [3:0]—mmmm)—its content encodesan implied leading opcode byte (OF, OF 38, or OF 3).

Data element width field 2564 (EVEX byte 2, bit [7]—W)—is represented bythe notation EVEX.W. EVEX.W is used to define the granularity (size) ofthe datatype (either 32-bit data elements or 64-bit data elements).

EVEX.vvvv 2620 (EVEX Byte 2, bits [6:3]-vvvv)—the role of EVEX.vvvv mayinclude the following: 1) EVEX.vvvv encodes the first source registeroperand, specified in inverted (1s complement) form and is valid forinstructions with 2 or more source operands; 2) EVEX.vvvv encodes thedestination register operand, specified in 1s complement form forcertain vector shifts; or 3) EVEX.vvvv does not encode any operand, thefield is reserved and should contain 1111b. Thus, EVEX.vvvv field 2620encodes the 4 low-order bits of the first source register specifierstored in inverted (1s complement) form. Depending on the instruction,an extra different EVEX bit field is used to extend the specifier sizeto 32 registers.

EVEX.0 2568 Class field (EVEX byte 2, bit [2]—U)—If EVEX.0=0, itindicates class A or EVEX.U0; if EVEX.0=1, it indicates class B orEVEX.U1.

Prefix encoding field 2625 (EVEX byte 2, bits [1:0]-pp)—providesadditional bits for the base operation field. In addition to providingsupport for the legacy SSE instructions in the EVEX prefix format, thisalso has the benefit of compacting the SIMD prefix (rather thanrequiring a byte to express the SIMD prefix, the EVEX prefix requiresonly 2 bits). In one embodiment, to support legacy SSE instructions thatuse a SIMD prefix (66H, F2H, F3H) in both the legacy format and in theEVEX prefix format, these legacy SIMD prefixes are encoded into the SIMDprefix encoding field; and at runtime are expanded into the legacy SIMDprefix prior to being provided to the decoder's PLA (so the PLA canexecute both the legacy and EVEX format of these legacy instructionswithout modification). Although newer instructions could use the EVEXprefix encoding field's content directly as an opcode extension, certainembodiments expand in a similar fashion for consistency but allow fordifferent meanings to be specified by these legacy SIMD prefixes. Analternative embodiment may redesign the PLA to support the 2-bit SIMDprefix encodings, and thus not require the expansion.

Alpha field 2552 (EVEX byte 3, bit [7]—EH; also known as EVEX.EH,EVEX.rs, EVEX.RL, EVEX.write mask control, and EVEX.N; also illustratedwith α)—as previously described, this field is context specific.

Beta field 2554 (EVEX byte 3, bits [6:4]—SSS, also known as EVEX.s₂₋₀,EVEX.r₂₋₀, EVEX.rr1, EVEX.LL0, EVEX.LLB; also illustrated with βββ)—aspreviously described, this field is context specific.

REX′ field 2510—this is the remainder of the REX′ field and is theEVEX.V′ bit field (EVEX Byte 3, bit [3]—V′) that may be used to encodeeither the upper 16 or lower 16 of the extended 32 register set. Thisbit is stored in bit inverted format. A value of 1 is used to encode thelower 16 registers. In other words, V′VVVV is formed by combiningEVEX.V′, EVEX.vvvv.

Write mask field 2570 (EVEX byte 3, bits [2:0]-kkk)—its contentspecifies the index of a register in the write mask registers aspreviously described. In one embodiment, the specific value EVEX.kkk=000has a special behavior implying no write mask is used for the particularinstruction (this may be implemented in a variety of ways including theuse of a write mask hardwired to all ones or hardware that bypasses themasking hardware).

Real Opcode Field 2630 (Byte 4) is also known as the opcode byte. Partof the opcode is specified in this field.

MOD R/M Field 2640 (Byte 5) includes MOD field 2642, Reg field 2644, andR/M field 2646. As previously described, the MOD field's 2642 contentdistinguishes between memory access and non-memory access operations.The role of Reg field 2644 can be summarized to two situations: encodingeither the destination register operand or a source register operand orbe treated as an opcode extension and not used to encode any instructionoperand. The role of R/M field 2646 may include the following: encodingthe instruction operand that references a memory address or encodingeither the destination register operand or a source register operand.

Scale, Index, Base (SIB) Byte (Byte 6)—As previously described, thecontent of SIB 2650 is used for memory address generation. SIB.xxx 2654and SIB.bbb 2656—the contents of these fields have been previouslyreferred to with regard to the register indexes Xxxx and Bbbb.

Displacement field 2562A (Bytes 7-10)—when MOD field 2642 contains 10,bytes 7-10 are the displacement field 2562A, and it works the same asthe legacy 32-bit displacement (disp32) and works at byte granularity.

Displacement factor field 2562B (Byte 7)—when MOD field 2642 contains01, byte 7 is the displacement factor field 2562B. The location of thisfield is that same as that of the legacy x86 instruction set 8-bitdisplacement (disp8), which works at byte granularity. Since disp8 issign extended, it can only address between −128 and 127 bytes offsets;in terms of 64-byte cache lines, disp8 uses 8 bits that can be set toonly four really useful values −128, −64, 0, and 64; since a greaterrange is often needed, disp32 is used; however, disp32 requires 4 bytes.In contrast to disp8 and disp32, the displacement factor field 2562B isa reinterpretation of disp8; when using displacement factor field 2562B,the actual displacement is determined by the content of the displacementfactor field multiplied by the size of the memory operand access (N).This type of displacement is referred to as disp8*N. This reduces theaverage instruction length (a single byte of used for the displacementbut with a much greater range). Such compressed displacement assumesthat the effective displacement is multiple of the granularity of thememory access, and hence, the redundant low-order bits of the addressoffset do not need to be encoded. In other words, the displacementfactor field 2562B substitutes the legacy x86 instruction set 8-bitdisplacement. Thus, the displacement factor field 2562B is encoded thesame way as an x86 instruction set 8-bit displacement (so no changes inthe ModRM/SIB encoding rules) with the only exception that disp8 isoverloaded to disp8*N. In other words, there are no changes in theencoding rules or encoding lengths but only in the interpretation of thedisplacement value by hardware (which needs to scale the displacement bythe size of the memory operand to obtain a byte-wise address offset).Immediate field 2572 operates as previously described.

Full Opcode Field

FIG. 26B is a block diagram illustrating the fields of the specificvector friendly instruction format 2600 that make up the full opcodefield 2574 according to one embodiment. Specifically, the full opcodefield 2574 includes the format field 2540, the base operation field2542, and the data element width (W) field 2564. The base operationfield 2542 includes the prefix encoding field 2625, the opcode map field2615, and the real opcode field 2630.

Register Index Field

FIG. 26C is a block diagram illustrating the fields of the specificvector friendly instruction format 2600 that make up the register indexfield 2544 according to one embodiment. Specifically, the register indexfield 2544 includes the REX 2605 field, the REX′ 2610 field, theMODR/M.reg field 2644, the MODR/M.r/m field 2646, the VVVV field 2620,xxx field 2654, and the bbb field 2656.

Augmentation Operation Field

FIG. 26D is a block diagram illustrating the fields of the specificvector friendly instruction format 2600 that make up the augmentationoperation field 2550 according to one embodiment. When the class (U)field 2568 contains 0, it signifies EVEX.U0 (class A 2568A); when itcontains 1, it signifies EVEX.U1 (class B 2568B). When U=0 and the MODfield 2642 contains 11 (signifying a no memory access operation), thealpha field 2552 (EVEX byte 3, bit [7]—EH) is interpreted as the rsfield 2552A. When the rs field 2552A contains a 1 (round 2552A.1), thebeta field 2554 (EVEX byte 3, bits [6:4]—SSS) is interpreted as theround control field 2554A. The round control field 2554A includes aone-bit SAE field 2556 and a two-bit round operation field 2558. Whenthe rs field 2552A contains a 0 (data transform 2552A.2), the beta field2554 (EVEX byte 3, bits [6:4]—SSS) is interpreted as a three-bit datatransform field 2554B. When U=0 and the MOD field 2642 contains 00, 01,or 10 (signifying a memory access operation), the alpha field 2552 (EVEXbyte 3, bit [7]—EH) is interpreted as the eviction hint (EH) field 2552Band the beta field 2554 (EVEX byte 3, bits [6:4]—SSS) is interpreted asa three bit data manipulation field 2554C.

When U=1, the alpha field 2552 (EVEX byte 3, bit [7]—EH) is interpretedas the write mask control (Z) field 2552C. When U=1 and the MOD field2642 contains 11 (signifying a no memory access operation), part of thebeta field 2554 (EVEX byte 3, bit [4]—S0) is interpreted as the RL field2557A; when it contains a 1 (round 2557A.1) the rest of the beta field2554 (EVEX byte 3, bit [6-5]—S₂₋₁) is interpreted as the round operationfield 2559A, while when the RL field 2557A contains a 0 (VSIZE 2557A.2)the rest of the beta field 2554 (EVEX byte 3, bit [6-5]—S₂₋₁) isinterpreted as the vector length field 2559B (EVEX byte 3, bit[6-5]—L₁₋₀). When U=1 and the MOD field 2642 contains 00, 01, or 10(signifying a memory access operation), the beta field 2554 (EVEX byte3, bits [6:4]—SSS) is interpreted as the vector length field 2559B (EVEXbyte 3, bit [6-5]—L₁₋₀) and the broadcast field 2557B (EVEX byte 3, bit[4]—B).

Exemplary Register Architecture

FIG. 27 is a block diagram of a register architecture 2700 according toone embodiment. In the embodiment illustrated, there are 32 vectorregisters 2710 that are 512 bits wide; these registers are referenced aszmm0 through zmm31. The lower order 256 bits of the lower 16 zmmregisters are overlaid on registers ymm0-16. The lower order 128 bits ofthe lower 16 zmm registers (the lower order 128 bits of the ymmregisters) are overlaid on registers xmm0-15. The specific vectorfriendly instruction format 2600 operates on these overlaid registerfile as illustrated in the below tables.

Adjustable Vector Length Class Operations Registers InstructionTemplates A (FIG. 25A; 2510, 2515, zmm registers (the vector length isthat do not include the U = 0) 2525, 2530 64 byte) vector length field B(FIG. 25B; 2512 zmm registers (the vector length is 2559B U = 1) 64byte) Instruction templates B (FIG. 25B; 2517, 2527 zmm, ymm, or xmmregisters (the that do include the U = 1) vector length is 64-byte,32-byte, vector length field or 16-byte) depending on the 2559B vectorlength field 2559B

In other words, the vector length field 2559B selects between a maximumlength and one or more other shorter lengths, where each such shorterlength is half the length of the preceding length; and instructionstemplates without the vector length field 2559B operate on the maximumvector length. Further, in one embodiment, the class B instructiontemplates of the specific vector friendly instruction format 2600operate on packed or scalar single/double-precision floating point dataand packed or scalar integer data. Scalar operations are operationsperformed on the lowest order data element position in a zmm/ymm/xmmregister; the higher order data element positions are either left thesame as they were prior to the instruction or zeroed depending on theembodiment.

Write mask registers 2715—in the embodiment illustrated, there are 8write mask registers (k0 through k7), each 64 bits in size. In analternate embodiment, the write mask registers 2715 are 16 bits in size.As previously described, in one embodiment, the vector mask register k0cannot be used as a write mask; when the encoding that would normallyindicate k0 is used for a write mask, it selects a hardwired write maskof 0xFFFF, effectively disabling write masking for that instruction.

General-purpose registers 2725—in the embodiment illustrated, there aresixteen 64-bit general-purpose registers that are used along with theexisting x86 addressing modes to address memory operands. Theseregisters are referenced by the names RAX, RBX, RCX, RDX, RBP, RSI, RDI,RSP, and R8 through R15.

Scalar floating point stack register file (x87 stack) 2745, on which isaliased the MMX packed integer flat register file 2750—in the embodimentillustrated, the x87 stack is an eight-element stack used to performscalar floating-point operations on 32/64/80-bit floating point datausing the x87 instruction set extension; while the MMX registers areused to perform operations on 64-bit packed integer data, as well as tohold operands for some operations performed between the MMX and XMMregisters.

Alternative embodiments may use wider or narrower registers.Additionally, alternative embodiments may use more, less, or differentregister files and registers.

Exemplary Core Architectures, Processors, and Computer Architectures

Processor cores may be implemented in different ways, for differentpurposes, and in different processors. For instance, implementations ofsuch cores may include: 1) a general purpose in-order core intended forgeneral-purpose computing; 2) a high performance general purposeout-of-order core intended for general-purpose computing; 3) a specialpurpose core intended primarily for graphics and/or scientific(throughput) computing. Implementations of different processors mayinclude: 1) a CPU including one or more general purpose in-order coresintended for general-purpose computing and/or one or more generalpurpose out-of-order cores intended for general-purpose computing; and2) a coprocessor including one or more special purpose cores intendedprimarily for graphics and/or scientific (throughput). Such differentprocessors lead to different computer system architectures, which mayinclude: 1) the coprocessor on a separate chip from the CPU; 2) thecoprocessor on a separate die in the same package as a CPU; 3) thecoprocessor on the same die as a CPU (in which case, such a coprocessoris sometimes referred to as special purpose logic, such as integratedgraphics and/or scientific (throughput) logic, or as special purposecores); and 4) a system on a chip that may include on the same die thedescribed CPU (sometimes referred to as the application core(s) orapplication processor(s)), the above described coprocessor, andadditional functionality. Exemplary core architectures are describednext, followed by descriptions of exemplary processors and computerarchitectures.

Exemplary Core Architectures

In-Order and Out-of-Order Core Block Diagram

FIG. 28A is a block diagram illustrating both an exemplary in-orderpipeline and an exemplary register renaming, out-of-orderissue/execution pipeline according to embodiments. FIG. 28B is a blockdiagram illustrating both an exemplary embodiment of an in-orderarchitecture core and an exemplary register renaming, out-of-orderissue/execution architecture core to be included in a processoraccording to embodiments. The solid lined boxes in FIGS. 28A-Billustrate the in-order pipeline and in-order core, while the optionaladdition of the dashed lined boxes illustrates the register renaming,out-of-order issue/execution pipeline and core. Given that the in-orderaspect is a subset of the out-of-order aspect, the out-of-order aspectwill be described.

In FIG. 28A, a processor pipeline 2800 includes a fetch stage 2802, alength decode stage 2804, a decode stage 2806, an allocation stage 2808,a renaming stage 2810, a scheduling (also known as a dispatch or issue)stage 2812, a register read/memory read stage 2814, an execute stage2816, a write back/memory write stage 2818, an exception handling stage2822, and a commit stage 2824.

FIG. 28B shows processor core 2890 including a front-end unit 2830coupled to an execution engine unit 2850, and both are coupled to amemory unit 2870. The core 2890 may be a reduced instruction setcomputing (RISC) core, a complex instruction set computing (CISC) core,a very long instruction word (VLIW) core, or a hybrid or alternativecore type. As yet another option, the core 2890 may be a special-purposecore, such as, for example, a network or communication core, compressionengine, coprocessor core, general purpose computing graphics processingunit (GPGPU) core, graphics core, or the like.

The front-end unit 2830 includes a branch prediction unit 2832 coupledto an instruction cache unit 2834, which is coupled to an instructiontranslation lookaside buffer (TLB) 2836, which is coupled to aninstruction fetch unit 2838, which is coupled to a decode unit 2840. Thedecode unit 2840 (or decoder) may decode instructions, and generate asan output one or more micro-operations, micro-code entry points,microinstructions, other instructions, or other control signals, whichare decoded from, or which otherwise reflect, or are derived from, theoriginal instructions. The decode unit 2840 may be implemented usingvarious different mechanisms. Examples of suitable mechanisms include,but are not limited to, look-up tables, hardware implementations,programmable logic arrays (PLAs), microcode read only memories (ROMs),etc. In one embodiment, the core 2890 includes a microcode ROM or othermedium that stores microcode for certain macroinstructions (e.g., indecode unit 2840 or otherwise within the front-end unit 2830). Thedecode unit 2840 is coupled to a rename/allocator unit 2852 in theexecution engine unit 2850.

The execution engine unit 2850 includes the rename/allocator unit 2852coupled to a retirement unit 2854 and a set of one or more schedulerunit(s) 2856. The scheduler unit(s) 2856 represents any number ofdifferent schedulers, including reservations stations, centralinstruction window, etc. The scheduler unit(s) 2856 is coupled to thephysical register file(s) unit(s) 2858. Each of the physical registerfile(s) units 2858 represents one or more physical register files,different ones of which store one or more different data types, such asscalar integer, scalar floating point, packed integer, packed floatingpoint, vector integer, vector floating point, status (e.g., aninstruction pointer that is the address of the next instruction to beexecuted), etc. In one embodiment, the physical register file(s) unit2858 comprises a vector registers unit, a write mask registers unit, anda scalar registers unit. These register units may provide architecturalvector registers, vector mask registers, and general-purpose registers.The physical register file(s) unit(s) 2858 is overlapped by theretirement unit 2854 to illustrate various ways in which registerrenaming and out-of-order execution may be implemented (e.g., using areorder buffer(s) and a retirement register file(s); using a futurefile(s), a history buffer(s), and a retirement register file(s); using aregister maps and a pool of registers; etc.). The retirement unit 2854and the physical register file(s) unit(s) 2858 are coupled to theexecution cluster(s) 2860. The execution cluster(s) 2860 includes a setof one or more execution units 2862 and a set of one or more memoryaccess units 2864. The execution units 2862 may perform variousoperations (e.g., shifts, addition, subtraction, multiplication) and onvarious types of data (e.g., scalar floating point, packed integer,packed floating point, vector integer, vector floating point). Whilesome embodiments may include a number of execution units dedicated tospecific functions or sets of functions, other embodiments may includeonly one execution unit or multiple execution units that all perform allfunctions. The scheduler unit(s) 2856, physical register file(s) unit(s)2858, and execution cluster(s) 2860 are shown as being possibly pluralbecause certain embodiments create separate pipelines for certain typesof data/operations (e.g., a scalar integer pipeline, a scalar floatingpoint/packed integer/packed floating point/vector integer/vectorfloating point pipeline, and/or a memory access pipeline that each havetheir own scheduler unit, physical register file(s) unit, and/orexecution cluster—and in the case of a separate memory access pipeline,certain embodiments are implemented in which only the execution clusterof this pipeline has the memory access unit(s) 2864). It should also beunderstood that where separate pipelines are used, one or more of thesepipelines may be out-of-order issue/execution and the rest in-order.

The set of memory access units 2864 is coupled to the memory unit 2870,which includes a data TLB unit 2872 coupled to a data cache unit 2874coupled to a level 2 (L2) cache unit 2876. In one exemplary embodiment,the memory access units 2864 may include a load unit, a store addressunit, and a store data unit, each of which is coupled to the data TLBunit 2872 in the memory unit 2870. The instruction cache unit 2834 isfurther coupled to a level 2 (L2) cache unit 2876 in the memory unit2870. The L2 cache unit 2876 is coupled to one or more other levels ofcache and eventually to a main memory.

By way of example, the exemplary register renaming, out-of-orderissue/execution core architecture may implement the pipeline 2800 asfollows: 1) the instruction fetch 2838 performs the fetch and lengthdecoding stages 2802 and 2804; 2) the decode unit 2840 performs thedecode stage 2806; 3) the rename/allocator unit 2852 performs theallocation stage 2808 and renaming stage 2810; 4) the scheduler unit(s)2856 performs the schedule stage 2812; 5) the physical register file(s)unit(s) 2858 and the memory unit 2870 perform the register read/memoryread stage 2814; the execution cluster 2860 perform the execute stage2816; 6) the memory unit 2870 and the physical register file(s) unit(s)2858 perform the write back/memory write stage 2818; 7) various unitsmay be involved in the exception handling stage 2822; and 8) theretirement unit 2854 and the physical register file(s) unit(s) 2858perform the commit stage 2824.

The core 2890 may support one or more instructions sets (e.g., the x86instruction set (with some extensions that have been added with newerversions); the MIPS instruction set of MIPS Technologies of Sunnyvale,Calif.; the ARM instruction set (with optional additional extensionssuch as NEON) of ARM Holdings of Sunnyvale, Calif.), including theinstruction(s) described herein. In one embodiment, the core 2890includes logic to support a packed data instruction set extension (e.g.,AVX1, AVX2), thereby allowing the operations used by many multimediaapplications to be performed using packed data.

It should be understood that the core may support multithreading(executing two or more parallel sets of operations or threads), and maydo so in a variety of ways including time sliced multithreading,simultaneous multithreading (where a single physical core provides alogical core for each of the threads that physical core issimultaneously multithreading), or a combination thereof (e.g., timesliced fetching and decoding and simultaneous multithreading thereaftersuch as in the Intel® Hyperthreading technology).

While register renaming is described in the context of out-of-orderexecution, it should be understood that register renaming may be used inan in-order architecture. While the illustrated embodiment of theprocessor also includes separate instruction and data cache units2834/2874 and a shared L2 cache unit 2876, alternative embodiments mayhave a single internal cache for both instructions and data, such as,for example, a Level 1 (L1) internal cache, or multiple levels ofinternal cache. In some embodiments, the system may include acombination of an internal cache and an external cache that is externalto the core and/or the processor. Alternatively, all of the cache may beexternal to the core and/or the processor.

Specific Exemplary in-Order Core Architecture

FIGS. 29A-B illustrate a block diagram of a more specific exemplaryin-order core architecture, which core would be one of several logicblocks (including other cores of the same type and/or different types)in a chip. The logic blocks communicate through a high-bandwidthinterconnect network (e.g., a ring network) with some fixed functionlogic, memory I/O interfaces, and other necessary I/O logic, dependingon the application.

FIG. 29A is a block diagram of a single processor core, along with itsconnection to the on-die interconnect network 2902 and with its localsubset of the Level 2 (L2) cache 2904, according to embodiments. In oneembodiment, an instruction decoder 2900 supports the x86 instruction setwith a packed data instruction set extension. An L1 cache 2906 allowslow-latency accesses to cache memory into the scalar and vector units.While in one embodiment (to simplify the design), a scalar unit 2908 anda vector unit 2910 use separate register sets (respectively, scalarregisters 2912 and vector registers 2914) and data transferred betweenthem is written to memory and then read back in from a level 1 (L1)cache 2906, alternative embodiments may use a different approach (e.g.,use a single register set or include a communication path that allowdata to be transferred between the two register files without beingwritten and read back).

The local subset of the L2 cache 2904 is part of a global L2 cache thatis divided into separate local subsets, one per processor core. Eachprocessor core has a direct access path to its own local subset of theL2 cache 2904. Data read by a processor core is stored in its L2 cachesubset 2904 and can be accessed quickly, in parallel with otherprocessor cores accessing their own local L2 cache subsets. Data writtenby a processor core is stored in its own L2 cache subset 2904 and isflushed from other subsets, if necessary. The ring network ensurescoherency for shared data. The ring network is bi-directional to allowagents such as processor cores, L2 caches and other logic blocks tocommunicate with each other within the chip. Each ring data-path is1012-bits wide per direction.

FIG. 29B is an expanded view of part of the processor core in FIG. 29Aaccording to embodiments. FIG. 29B includes an L1 data cache 2906A partof the L1 cache 2904, as well as more detail regarding the vector unit2910 and the vector registers 2914. Specifically, the vector unit 2910is a 16-wide vector processing unit (VPU) (see the 16-wide ALU 2928),which executes one or more of integer, single-precision float, anddouble-precision float instructions. The VPU supports swizzling theregister inputs with swizzle unit 2920, numeric conversion with numericconvert units 2922A-B, and replication with replication unit 2924 on thememory input. Write mask registers 2926 allow predicating resultingvector writes.

FIG. 30 is a block diagram of a processor 3000 that may have more thanone core, may have an integrated memory controller, and may haveintegrated graphics according to embodiments. The solid lined boxes inFIG. 30 illustrate a processor 3000 with a single core 3002A, a systemagent 3010, a set of one or more bus controller units 3016, while theoptional addition of the dashed lined boxes illustrates an alternativeprocessor 3000 with multiple cores 3002A-N, a set of one or moreintegrated memory controller unit(s) 3014 in the system agent unit 3010,and special purpose logic 3008.

Thus, different implementations of the processor 3000 may include: 1) aCPU with the special purpose logic 3008 being integrated graphics and/orscientific (throughput) logic (which may include one or more cores), andthe cores 3002A-N being one or more general purpose cores (e.g., generalpurpose in-order cores, general purpose out-of-order cores, acombination of the two); 2) a coprocessor with the cores 3002A-N being alarge number of special purpose cores intended primarily for graphicsand/or scientific (throughput); and 3) a coprocessor with the cores3002A-N being a large number of general purpose in-order cores. Thus,the processor 3000 may be a general-purpose processor, coprocessor orspecial-purpose processor, such as, for example, a network orcommunication processor, compression engine, graphics processor, GPGPU(general purpose graphics processing unit), a high-throughput manyintegrated core (MIC) coprocessor (including 30 or more cores), embeddedprocessor, or the like. The processor may be implemented on one or morechips. The processor 3000 may be a part of and/or may be implemented onone or more substrates using any of a number of process technologies,such as, for example, BiCMOS, CMOS, or NMOS.

The memory hierarchy includes one or more levels of cache within thecores, a set or one or more shared cache units 3006, and external memory(not shown) coupled to the set of integrated memory controller units3014. The set of shared cache units 3006 may include one or moremid-level caches, such as level 2 (L2), level 3 (L3), level 4 (L4), orother levels of cache, a last level cache (LLC), and/or combinationsthereof. While in one embodiment a ring based interconnect unit 3012interconnects the special purpose logic 3008 (integrated graphics logicis an example of and is also referred to herein as special purposelogic), the set of shared cache units 3006, and the system agent unit3010/integrated memory controller unit(s) 3014, alternative embodimentsmay use any number of well-known techniques for interconnecting suchunits. In one embodiment, coherency is maintained between one or morecache units 3006 and cores 3002A-N.

In some embodiments, one or more of the cores 3002A-N are capable ofmultithreading. The system agent 3010 includes those componentscoordinating and operating cores 3002A-N. The system agent unit 3010 mayinclude for example a power control unit (PCU) and a display unit. ThePCU may be or include logic and components needed for regulating thepower state of the cores 3002A-N and the special purpose logic 3008. Thedisplay unit is for driving one or more externally connected displays.

The cores 3002A-N may be homogenous or heterogeneous in terms ofarchitecture instruction set; that is, two or more of the cores 3002A-Nmay be capable of execution the same instruction set, while others maybe capable of executing only a subset of that instruction set or adifferent instruction set.

Exemplary Computer Architectures

FIGS. 31-34 are block diagrams of exemplary computer architectures.Other system designs and configurations known in the arts for laptops,desktops, handheld PCs, personal digital assistants, engineeringworkstations, servers, network devices, network hubs, switches, embeddedprocessors, digital signal processors (DSPs), graphics devices, videogame devices, set-top boxes, micro controllers, cell phones, portablemedia players, hand held devices, and various other electronic devices,are also suitable. In general, a huge variety of systems or electronicdevices capable of incorporating a processor and/or other executionlogic as disclosed herein are generally suitable.

Referring now to FIG. 31, shown is a block diagram of a system 3100 inaccordance with one embodiment of the present invention. The system 3100may include one or more processors 3110, 3115, which are coupled to acontroller hub 3120. In one embodiment the controller hub 3120 includesa graphics memory controller hub (GMCH) 3190 and an Input/Output Hub(IOH) 3150 (which may be on separate chips); the GMCH 3190 includesmemory and graphics controllers to which are coupled memory 3140 and acoprocessor 3145; the IOH 3150 couples input/output (I/O) devices 3160to the GMCH 3190. Alternatively, one or both of the memory and graphicscontrollers are integrated within the processor (as described herein),the memory 3140 and the coprocessor 3145 are coupled directly to theprocessor 3110, and the controller hub 3120 in a single chip with theIOH 3150.

The optional nature of additional processors 3115 is denoted in FIG. 31with broken lines. Each processor 3110, 3115 may include one or more ofthe processing cores described herein and may be some version of theprocessor 3000.

The memory 3140 may be, for example, dynamic random-access memory(DRAM), phase change memory (PCM), or a combination of the two. For atleast one embodiment, the controller hub 3120 communicates with theprocessor(s) 3110, 3115 via a multi-drop bus, such as a frontside bus(FSB), point-to-point interface such as QuickPath Interconnect (QPI), orsimilar connection 3195.

In one embodiment, the coprocessor 3145 is a special-purpose processor,such as, for example, a high-throughput MIC processor, a network orcommunication processor, compression engine, graphics processor, GPGPU,embedded processor, or the like. In one embodiment, controller hub 3120may include an integrated graphics accelerator.

There can be a variety of differences between the physical resources3110, 3115 in terms of a spectrum of metrics of merit includingarchitectural, microarchitectural, thermal, power consumptioncharacteristics, and the like.

In one embodiment, the processor 3110 executes instructions that controldata processing operations of a general type. Embedded within theinstructions may be coprocessor instructions. The processor 3110recognizes these coprocessor instructions as being of a type that shouldbe executed by the attached coprocessor 3145. Accordingly, the processor3110 issues these coprocessor instructions (or control signalsrepresenting coprocessor instructions) on a coprocessor bus or otherinterconnect, to coprocessor 3145. Coprocessor(s) 3145 accept andexecute the received coprocessor instructions.

Referring now to FIG. 32, shown is a block diagram of a first morespecific exemplary system 3200 in accordance with an embodiment of thepresent invention. As shown in FIG. 32, multiprocessor system 3200 is apoint-to-point interconnect system, and includes a first processor 3270and a second processor 3280 coupled via a point-to-point interconnect3250. Each of processors 3270 and 3280 may be some version of theprocessor 3000. In one embodiment, processors 3270 and 3280 arerespectively processors 3110 and 3115, while coprocessor 3238 iscoprocessor 3145. In another embodiment, processors 3270 and 3280 arerespectively processor 3110 coprocessor 3145.

Processors 3270 and 3280 are shown including integrated memorycontroller (IMC) units 3272 and 3282, respectively. Processor 3270 alsoincludes as part of its bus controller units point-to-point (P-P)interfaces 3276 and 3278; similarly, second processor 3280 includes P-Pinterfaces 3286 and 3288. Processors 3270, 3280 may exchange informationvia a point-to-point (P-P) interface 3250 using P-P interface circuits3278, 3288. As shown in FIG. 32, IMCs 3272 and 3282 couple theprocessors to respective memories, namely a memory 3232 and a memory3234, which may be portions of main memory locally attached to therespective processors.

Processors 3270, 3280 may each exchange information with a chipset 3290via individual P-P interfaces 3252, 3254 using point to point interfacecircuits 3276, 3294, 3286, 3298. Chipset 3290 may optionally exchangeinformation with the coprocessor 3238 via a high-performance interface3292. In one embodiment, the coprocessor 3238 is a special-purposeprocessor, such as, for example, a high-throughput MIC processor, anetwork or communication processor, compression engine, graphicsprocessor, GPGPU, embedded processor, or the like.

A shared cache (not shown) may be included in either processor oroutside of both processors yet connected with the processors via P-Pinterconnect, such that either or both processors' local cacheinformation may be stored in the shared cache if a processor is placedinto a low power mode.

Chipset 3290 may be coupled to a first bus 3216 via an interface 3296.In one embodiment, first bus 3216 may be a Peripheral ComponentInterconnect (PCI) bus, or a bus such as a PCI Express bus or anotherthird generation I/O interconnect bus, although the scope of the presentinvention is not so limited.

As shown in FIG. 32, various I/O devices 3214 may be coupled to firstbus 3216, along with a bus bridge 3218 which couples first bus 3216 to asecond bus 3220. In one embodiment, one or more additional processor(s)3215, such as coprocessors, high-throughput MIC processors, GPGPU's,accelerators (such as, e.g., graphics accelerators or digital signalprocessing (DSP) units), field programmable gate arrays, or any otherprocessor, are coupled to first bus 3216. In one embodiment, second bus3220 may be a low pin count (LPC) bus. Various devices may be coupled toa second bus 3220 including, for example, a keyboard and/or mouse 3222,communication devices 3227 and a storage unit 3228 such as a disk driveor other mass storage device which may include instructions/code anddata 3230, in one embodiment. Further, an audio I/O 3224 may be coupledto the second bus 3220. Note that other architectures are possible. Forexample, instead of the point-to-point architecture of FIG. 32, a systemmay implement a multi-drop bus or other such architecture.

Referring now to FIG. 33, shown is a block diagram of a second morespecific exemplary system 3300 in accordance with an embodiment of thepresent invention. Like elements in FIGS. 32 and 33 bear like referencenumerals, and certain aspects of FIG. 32 have been omitted from FIG. 33in order to avoid obscuring other aspects of FIG. 33.

FIG. 33 illustrates that the processors 3270, 3280 may includeintegrated memory and I/O control logic (“CL”) 3372 and 3382,respectively. Thus, the CL 3372, 3382 include integrated memorycontroller units and include I/O control logic. FIG. 33 illustrates thatnot only are the memories 3232, 3234 coupled to the CL 3372, 3382, butalso that I/O devices 3314 are also coupled to the control logic 3372,3382. Legacy I/O devices 3315 are coupled to the chipset 3290.

Referring now to FIG. 34, shown is a block diagram of a SoC 3400 inaccordance with an embodiment of the present invention. Similar elementsin FIG. 30 bear like reference numerals. Also, dashed lined boxes areoptional features on more advanced SoCs. In FIG. 34, an interconnectunit(s) 3402 is coupled to: an application processor 3410 which includesa set of one or more cores 3002A-N, which include cache units 3004A-N,and shared cache unit(s) 3006; a system agent unit 3010; a buscontroller unit(s) 3016; an integrated memory controller unit(s) 3014; aset or one or more coprocessors 3420 which may include integratedgraphics logic, an image processor, an audio processor, and a videoprocessor; an static random access memory (SRAM) unit 3430; a directmemory access (DMA) unit 3432; and a display unit 3440 for coupling toone or more external displays. In one embodiment, the coprocessor(s)3420 include a special-purpose processor, such as, for example, anetwork or communication processor, compression engine, GPGPU, ahigh-throughput MIC processor, embedded processor, or the like.

Embodiments of the mechanisms disclosed herein may be implemented inhardware, software, firmware, or a combination of such implementationapproaches. Embodiments may be implemented as computer programs orprogram code executing on programmable systems comprising at least oneprocessor, a storage system (including volatile and non-volatile memoryand/or storage elements), at least one input device, and at least oneoutput device.

Program code, such as code 3230 illustrated in FIG. 32, may be appliedto input instructions to perform the functions described herein andgenerate output information. The output information may be applied toone or more output devices, in known fashion. For purposes of thisapplication, a processing system includes any system that has aprocessor, such as, for example; a digital signal processor (DSP), amicrocontroller, an application specific integrated circuit (ASIC), or amicroprocessor.

The program code may be implemented in a high level procedural orobject-oriented programming language to communicate with a processingsystem. The program code may also be implemented in assembly or machinelanguage, if desired. In fact, the mechanisms described herein are notlimited in scope to any particular programming language. In any case,the language may be a compiled or interpreted language.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

Such machine-readable storage media may include, without limitation,non-transitory, tangible arrangements of articles manufactured or formedby a machine or device, including storage media such as hard disks, anyother type of disk including floppy disks, optical disks, compact diskread-only memories (CD-ROMs), compact disk rewritable's (CD-RWs), andmagneto-optical disks, semiconductor devices such as read-only memories(ROMs), random access memories (RAMs) such as dynamic random accessmemories (DRAMs), static random access memories (SRAMs), erasableprogrammable read-only memories (EPROMs), flash memories, electricallyerasable programmable read-only memories (EEPROMs), phase change memory(PCM), magnetic or optical cards, or any other type of media suitablefor storing electronic instructions.

Accordingly, embodiments also include non-transitory, tangiblemachine-readable media containing instructions or containing designdata, such as Hardware Description Language (HDL), which definesstructures, circuits, apparatuses, processors and/or system featuresdescribed herein. Such embodiments may also be referred to as programproducts.

Emulation (Including Binary Translation, Code Morphing, Etc.)

In some cases, an instruction converter may be used to convert aninstruction from a source instruction set to a target instruction set.For example, the instruction converter may translate (e.g., using staticbinary translation, dynamic binary translation including dynamiccompilation), morph, emulate, or otherwise convert an instruction to oneor more other instructions to be processed by the core. The instructionconverter may be implemented in software, hardware, firmware, or acombination thereof. The instruction converter may be on processor, offprocessor, or part on and part off processor.

FIG. 35 is a block diagram contrasting the use of a software instructionconverter to convert binary instructions in a source instruction set tobinary instructions in a target instruction set according toembodiments. In the illustrated embodiment, the instruction converter isa software instruction converter, although alternatively the instructionconverter may be implemented in software, firmware, hardware, or variouscombinations thereof. FIG. 35 shows a program in a high-level language3502 may be compiled using an x86 compiler 3504 to generate x86 binarycode 3506 that may be natively executed by a processor with at least onex86 instruction set core 3516. The processor with at least one x86instruction set core 3516 represents any processor that can performsubstantially the same functions as an Intel processor with at least onex86 instruction set core by compatibly executing or otherwise processing(1) a substantial portion of the instruction set of the Intel x86instruction set core or (2) object code versions of applications orother software targeted to run on an Intel processor with at least onex86 instruction set core, in order to achieve substantially the sameresult as an Intel processor with at least one x86 instruction set core.The x86 compiler 3504 represents a compiler that is operable to generatex86 binary code 3506 (e.g., object code) that can, with or withoutadditional linkage processing, be executed on the processor with atleast one x86 instruction set core 3516. Similarly, FIG. 35 shows theprogram in the high level language 3502 may be compiled using analternative instruction set compiler 3508 to generate alternativeinstruction set binary code 3510 that may be natively executed by aprocessor without at least one x86 instruction set core 3514 (e.g., aprocessor with cores that execute the MIPS instruction set of MIPSTechnologies of Sunnyvale, Calif. and/or that execute the ARMinstruction set of ARM Holdings of Sunnyvale, Calif.). The instructionconverter 3512 is used to convert the x86 binary code 3506 into codethat may be natively executed by the processor without an x86instruction set core 3514. This converted code is not likely to be thesame as the alternative instruction set binary code 3510 because aninstruction converter capable of this is difficult to make; however, theconverted code will accomplish the general operation and be made up ofinstructions from the alternative instruction set. Thus, the instructionconverter 3512 represents software, firmware, hardware, or a combinationthereof that, through emulation, simulation or any other process, allowsa processor or other electronic device that does not have an x86instruction set processor or core to execute the x86 binary code 3506.

FURTHER EXAMPLES

Example 1 provides an exemplary processor comprising: decode circuitryto decode an instruction having fields to specify an opcode andlocations of first source, second source, and destination matrices, theopcode indicating that the processor is to multiply each element at rowM and column K of the first source matrix with a corresponding elementat row K and column N of the second source matrix, and accumulate aresulting product with previous contents of a corresponding element atrow M and column N of the destination matrix, the processor to skipmultiplications that, based on detected values of correspondingmultiplicands, would generate inconsequential results; schedulingcircuitry to schedule execution of the instruction; and executioncircuitry to execute the instructions as per the opcode.

Example 2 includes the substance of the exemplary processor of Example1, wherein the processor is to determine a multiplication that wouldgenerate inconsequential results by detecting that at least one of themultiplicands and multiplier has an absolute value that is either zeroor is lower than a predetermined threshold.

Example 3 includes the substance of the exemplary processor of Example1, wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding multiplying by a zero-valued element.

Example 4 includes the substance of the exemplary processor of Example1, wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding multiplying by a multiplicand having anabsolute value below a threshold, the threshold either to be specifiedby the instruction or to be programmed in a configuration register.

Example 5 includes the substance of the exemplary processor of Example1, wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding multiplying by a multiplicand having a value,an absolute difference between the value and a constant value beingbelow a threshold, the threshold and the constant value to each eitherto be specified by the instruction or to be programmed in aconfiguration register.

Example 6 includes the substance of the exemplary processor of claim 1,wherein read circuitry is to generate the detected values when readingthe corresponding elements.

Example 7 includes the substance of the exemplary processor of Example1, wherein each element of the first and second source matrices has afield indicating whether the element would generate an inconsequentialresult when used as a multiplicand in a multiplication.

Example 8 includes the substance of the exemplary processor of Example1, wherein the instruction further specifies a multibit mask having onebit per first source matrix element, the bit indicating whether theelement would generate an inconsequential result when used as amultiplicand in a multiplication.

Example 9 includes the substance of the exemplary processor of Example1, wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding scheduling execution resources to performsuch multiplications, and, instead, attempting to improve performance byscheduling the execution resource to perform multiplications on otherpairs of corresponding elements that would generate useful results.

Example 10 includes the substance of the exemplary processor of Example1, wherein avoiding multiplications that would generate inconsequentialresults comprises attempting to reduce power consumption by disablingexecution resources that would be used to perform the multiplications.

Example 11 provides an exemplary method to be performed by a processor,the processor comprising: decode circuitry to decode an instructionhaving fields to specify an opcode and locations of first source, secondsource, and destination matrices, the opcode indicating that theprocessor is to multiply each element at row M and column K of the firstsource matrix with a corresponding element at row K and column N of thesecond source matrix, and accumulate a resulting product with previouscontents of a corresponding element at row M and column N of thedestination matrix, the processor to skip multiplications that, based ondetected values of corresponding multiplicands, would generateinconsequential results; scheduling circuitry to schedule execution ofthe instruction; and execution circuitry to execute the instructions asper the opcode.

Example 12 includes the substance of the exemplary method of Example 11,wherein the processor is to determine a multiplication that wouldgenerate inconsequential results by detecting that at least one of themultiplicands and multiplier has an absolute value that is either zeroor is lower than a predetermined threshold.

Example 13 includes the substance of the exemplary method of Example 11,wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding multiplying by a zero-valued element.

Example 14 includes the substance of the exemplary method of Example 11,wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding multiplying by a multiplicand having anabsolute value below a threshold, the threshold either to be specifiedby the instruction or to be programmed in a configuration register.

Example 15 includes the substance of the exemplary method of Example 11,wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding multiplying by a multiplicand having a value,an absolute difference between the value and a constant value beingbelow a threshold, the threshold and the constant value to each eitherto be specified by the instruction or to be programmed in aconfiguration register.

Example 16 includes the substance of the exemplary method of Example 11,wherein read circuitry of the processor is to generate the detectedvalues when reading the corresponding elements.

Example 17 includes the substance of the exemplary method of Example 11,wherein each element of the first and second source matrices has a fieldindicating whether it would generate an inconsequential result when usedas a multiplicand in a multiplication.

Example 18 includes the substance of the exemplary method of Example 11,wherein the instruction further specifies a multibit mask having one bitper first source matrix element, the bit indicating whether the elementwould generate an inconsequential result when used as a multiplicand ina multiplication.

Example 19 includes the substance of the exemplary method of Example 11,wherein avoiding multiplications that would generate inconsequentialresults comprises avoiding scheduling execution resources to performsuch multiplications, and, instead, attempting to improve performance byscheduling the execution resource to perform multiplications on otherpairs of corresponding elements that would generate useful results.

Example 20 includes the substance of the exemplary method of Example 11,wherein avoiding multiplications that would generate inconsequentialresults comprises attempting to reduce power consumption by disablingexecution resources that would be used to perform the multiplications.

Example 21 provides an exemplary non-transitory machine-readable mediumcontaining instructions to which a processor is to respond by: decodingan instruction using decode circuitry, the instruction specifying anopcode and locations of first source, second source, and destinationmatrices, the opcode indicating that the processor is to multiply eachelement at row M and column K of the first source matrix with acorresponding element at row K and column N of the second source matrix,and accumulate a resulting product with previous contents of acorresponding element at row M and column N of the destination matrix,the processor to skip multiplications that, based on detected values ofcorresponding multiplicands, would generate inconsequential results;scheduling, using scheduling circuitry, execution of the instruction;and executing, using execution circuitry, the instructions as per theopcode.

Example 22 includes the substance of the exemplary non-transitorymachine-readable medium of claim 21, wherein the processor is todetermine a multiplication that would generate inconsequential resultsby detecting that at least one of the multiplicands and multiplier hasan absolute value that is either zero or is lower than a predeterminedthreshold.

Example 23 includes the substance of the exemplary non-transitorymachine-readable medium of Example 21, wherein avoiding multiplicationsthat would generate inconsequential results comprises avoidingmultiplying by a zero-valued element.

Example 24 includes the substance of the exemplary non-transitorymachine-readable medium of Example 21, wherein avoiding multiplicationsthat would generate inconsequential results comprises avoidingmultiplying by a multiplicand having an absolute value below athreshold, the threshold either to be specified by the instruction or tobe programmed in a configuration register.

Example 25 includes the substance of the exemplary non-transitorymachine-readable medium of Example 21, wherein avoiding multiplicationsthat would generate inconsequential results comprises avoidingmultiplying by a multiplicand having an absolute value below athreshold, the threshold either to be specified by the instruction or tobe programmed in a configuration register.

What is claimed is:
 1. A processor comprising: a matrix operationsaccelerator comprising a two-dimensional grid of fused multiplyaccumulate circuits; decode circuitry to decode a single instructionhaving fields to specify an opcode and locations of a first sourcematrix, a second source matrix, and a destination matrix that is asingle two-dimensional tile register in the matrix operationsaccelerator, the opcode indicating that execution circuitry is to causethe matrix operations accelerator to detect close to but non zero valuesof corresponding elements of the first source matrix and the secondsource matrix that would generate inconsequential results whenmultiplied, skip multiplications that would generate inconsequentialresults based on the detected close to but non zero values of thecorresponding elements by disabling a corresponding fused multiplyaccumulate circuit of the two-dimensional grid of fused multiplyaccumulate circuits, multiply other elements of the first source matrixwith a corresponding other element of the second source matrix, andaccumulate a resulting product with previous contents of a correspondingelement of the destination matrix in the single two-dimensional tileregister by enabling a corresponding fused multiply accumulate circuitof the two-dimensional grid of fused multiply accumulate circuits togenerate a resultant, and store the resultant in the correspondingelement in the single two-dimensional tile register; schedulingcircuitry to schedule execution of the single instruction; and theexecution circuitry to execute the single instruction as per the opcode.2. The processor of claim 1, wherein the execution circuitry is to causethe matrix operations accelerator to detect the close to but non zerovalues of corresponding elements of the first source matrix and thesecond source matrix that would generate inconsequential results whenmultiplied by detecting that at least one of the close to but non zerovalues of the corresponding elements has an absolute value that is lowerthan a predetermined threshold.
 3. The processor of claim 1, wherein theopcode further indicates the execution circuitry is to cause the matrixoperations accelerator to skip multiplying by the close to but non zerovalued elements of the first source matrix and the second source matrix.4. The processor of claim 2, wherein the predetermined threshold is tobe programmed in a configuration register of the matrix operationsaccelerator.
 5. The processor of claim 1, wherein the skip ofmultiplications that would generate inconsequential results comprisesskipping multiplying by at least one corresponding element of the firstsource matrix and the second source matrix having a value, an absolutedifference between the value and a constant value being below athreshold, the threshold and the constant value to each either to bespecified by the single instruction or to be programmed in aconfiguration register.
 6. The processor of claim 1, wherein the matrixoperations accelerator comprises read circuitry to detect, when readingthe corresponding elements, the close to but non zero values.
 7. Theprocessor of claim 1, wherein each element of the first and secondsource matrices has a field indicating whether the element is a close tobut non zero value.
 8. The processor of claim 1, wherein the singleinstruction further specifies a multibit mask having one bit per firstsource matrix element, the bit indicating whether the element is a closeto but non zero value.
 9. The processor of claim 1, wherein thedisabling the corresponding fused multiply accumulate circuit compriseshalting a clock of the corresponding fused multiply accumulate circuit.10. The processor of claim 1, wherein the disabling the correspondingfused multiply accumulate circuit comprises halting a supply voltage ofthe corresponding fused multiply accumulate circuit.
 11. A method to beperformed by a processor, the method comprising: decoding a singleinstruction using decode circuitry of the processor comprising a matrixoperations accelerator comprising a two-dimensional grid of fusedmultiply accumulate circuits, the single instruction having fields tospecify an opcode and locations of a first source matrix, a secondsource matrix, and a destination matrix that is a single two-dimensionaltile register in the matrix operations accelerator, the opcodeindicating that execution circuitry is to cause the matrix operationsaccelerator to detect close to but non zero values of correspondingelements of the first source matrix and the second source matrix thatwould generate inconsequential results when multiplied, skipmultiplications that would generate inconsequential results based on thedetected close to but non zero values of the corresponding elements bydisabling a corresponding fused multiply accumulate circuit of thetwo-dimensional grid of fused multiply accumulate circuits, multiplyother elements of the first source matrix with a corresponding otherelement of the second source matrix, and accumulate a resulting productwith previous contents of a corresponding element of the destinationmatrix in the single two-dimensional tile register by enabling acorresponding fused multiply accumulate circuit of the two-dimensionalgrid of fused multiply accumulate circuits to generate a resultant, andstore the resultant in the corresponding element in the singletwo-dimensional tile register scheduling, using scheduling circuitry,execution of the single instruction; and executing, using the executioncircuitry, the single instruction as per the opcode.
 12. The method ofclaim 11, wherein the execution circuitry is to cause the matrixoperations accelerator to detect the close to but non zero values ofcorresponding elements of the first source matrix and the second sourcematrix that would generate inconsequential results when multiplied bydetecting that at least one of the close to but non zero values of thecorresponding elements has an absolute value that is lower than apredetermined threshold.
 13. The method of claim 11, wherein the opcodefurther indicates the execution circuitry is to cause the matrixoperations accelerator to skip multiplying by the close to but non zerovalued elements of the first source matrix and the second source matrix.14. The method of claim 12, wherein the predetermined threshold isprogrammed in a configuration register of the matrix operationsaccelerator.
 15. The method of claim 11, wherein the skip ofmultiplications that would generate inconsequential results comprisesskipping multiplying by at least one corresponding element of the firstsource matrix and the second source matrix having a value, an absolutedifference between the value and a constant value being below athreshold, the threshold and the constant value to each either to bespecified by the single instruction or to be programmed in aconfiguration register.
 16. The method of claim 11, wherein the matrixoperations accelerator comprises read circuitry to detect, when readingthe corresponding elements, the close to but non zero values.
 17. Themethod of claim 11, wherein each element of the first and second sourcematrices has a field indicating whether the element is a close to butnon zero value.
 18. The method of claim 11, wherein the singleinstruction further specifies a multibit mask having one bit per firstsource matrix element, the bit indicating whether the element is a closeto but non zero value.
 19. The method of claim 11, wherein the disablingthe corresponding fused multiply accumulate circuit comprises halting aclock of the corresponding fused multiply accumulate circuit.
 20. Themethod of claim 11, wherein the disabling the corresponding fusedmultiply accumulate circuit comprises halting a supply voltage of thecorresponding fused multiply accumulate circuit.
 21. A non-transitorymachine-readable medium containing code to which a processor is torespond by: decoding a single instruction using decode circuitry of theprocessor comprising a matrix operations accelerator comprising atwo-dimensional grid of fused multiply accumulate circuits, the singleinstruction specifying an opcode and locations of a first source matrix,a second source matrix, and a destination matrix that is a singletwo-dimensional tile register in the matrix operations accelerator, theopcode indicating that execution circuitry is to cause the matrixoperations accelerator to detect close to but non zero values ofcorresponding elements of the first source matrix and the second sourcematrix that would generate inconsequential results when multiplied, skipmultiplications that would generate inconsequential results based on thedetected close to but non zero values of the corresponding elements bydisabling a corresponding fused multiply accumulate circuit of thetwo-dimensional grid of fused multiply accumulate circuits, multiplyother elements of the first source matrix with a corresponding otherelement of the second source matrix, and accumulate a resulting productwith previous contents of a corresponding element of the destinationmatrix in the single two-dimensional tile register by enabling acorresponding fused multiply accumulate circuit of the two-dimensionalgrid of fused multiply accumulate circuits to generate a resultant, andstore the resultant in the corresponding element in the singletwo-dimensional tile register scheduling, using scheduling circuitry,execution of the single instruction; and executing, using the executioncircuitry, the single instruction as per the opcode.
 22. Thenon-transitory machine-readable medium of claim 21, wherein theexecution circuitry is to cause the matrix operations accelerator todetect the close to but non zero values of corresponding elements of thefirst source matrix and the second source matrix that would generateinconsequential results when multiplied by detecting that at least oneof the close to but non zero values of the corresponding elements has anabsolute value that lower than a predetermined threshold.
 23. Thenon-transitory machine-readable medium of claim 21, wherein the opcodefurther indicates the execution circuitry is to cause the matrixoperations accelerator to skip multiplying by the close to but non zerovalued elements of the first source matrix and the second source matrix.24. The non-transitory machine-readable medium of claim 22, wherein thethreshold is programmed in a configuration register.
 25. Thenon-transitory machine-readable medium of claim 21, wherein the skip ofmultiplications that would generate inconsequential results comprisesskipping multiplying by at least one corresponding element of the firstsource matrix and the second source matrix having value an absolutedifference between the value and a constant value being below athreshold, the threshold and the constant value to each either to bespecified by the single instruction or to be programmed in aconfiguration register.